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North Atlantic right whales (Eubalaena glacialis), listed as endangered under the U.S. Endangered Species Act, migrate from the southeastern U.S.... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_48442171" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Species' conservation relies on understanding their seasonal habitats and migration routes. North Atlantic right whales (Eubalaena glacialis), listed as endangered under the U.S. Endangered Species Act, migrate from the southeastern U.S. coast to Cape Cod Bay, Massachusetts, a federally designated critical habitat, from February through May to feed. The whales then continue north across the Gulf of Maine to northern waters (e.g., Bay of Fundy). To enter Cape Cod Bay, right whales must traverse an area of dense shipping and fishing activity in Massachusetts Bay, where there are no mandatory regulations for the protection of right whales or management of their habitat. We used passive acoustic recordings of right whales collected in Massachusetts Bay from May 2007 through October 2010 to determine the annual spatial and temporal distribution of the whales and their calling activity. We detected right whales in the bay throughout the year, in contrast to results from visual surveys. Right whales were detected on at least 24% of days in each month, with the exception of June 2007, in which there were no detections. Averaged over all years, right whale calls were most abundant from February through May. During this period, calls were most frequent between 17:00 and 20:00 local time; no diel pattern was apparent in other months. The spatial distribution of the approximate locations of calling whales suggests they may use Massachusetts Bay as a conduit to Cape Cod Bay in the spring and as they move between the Gulf of Maine and waters to the south in September through December. Although it is unclear how dependent right whales are on the bay, the discovery of their widespread presence in Massachusetts Bay throughout the year suggests this region may need to be managed to reduce the probability of collisions with ships and entanglement in fishing gear.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/48442171" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f95114be7aa4ea5cdcabd7339376fc75" rel="nofollow" data-download="{"attachment_id":67051559,"asset_id":48442171,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67051559/download_file?st=MTczOTkxNzc0NCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="85871" href="https://cornell.academia.edu/AaronRice">Aaron Rice</a><script data-card-contents-for-user="85871" type="text/json">{"id":85871,"first_name":"Aaron","last_name":"Rice","domain_name":"cornell","page_name":"AaronRice","display_name":"Aaron Rice","profile_url":"https://cornell.academia.edu/AaronRice","photo":"https://0.academia-photos.com/85871/2816415/11310789/s65_aaron.rice.png"}</script></span></span></li><li class="js-paper-rank-work_48442171 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="48442171"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 48442171, container: ".js-paper-rank-work_48442171", }); 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North Atlantic right whales (Eubalaena glacialis), listed as endangered under the U.S. Endangered Species Act, migrate from the southeastern U.S. coast to Cape Cod Bay, Massachusetts, a federally designated critical habitat, from February through May to feed. The whales then continue north across the Gulf of Maine to northern waters (e.g., Bay of Fundy). To enter Cape Cod Bay, right whales must traverse an area of dense shipping and fishing activity in Massachusetts Bay, where there are no mandatory regulations for the protection of right whales or management of their habitat. We used passive acoustic recordings of right whales collected in Massachusetts Bay from May 2007 through October 2010 to determine the annual spatial and temporal distribution of the whales and their calling activity. We detected right whales in the bay throughout the year, in contrast to results from visual surveys. Right whales were detected on at least 24% of days in each month, with the exception of June 2007, in which there were no detections. Averaged over all years, right whale calls were most abundant from February through May. During this period, calls were most frequent between 17:00 and 20:00 local time; no diel pattern was apparent in other months. The spatial distribution of the approximate locations of calling whales suggests they may use Massachusetts Bay as a conduit to Cape Cod Bay in the spring and as they move between the Gulf of Maine and waters to the south in September through December. Although it is unclear how dependent right whales are on the bay, the discovery of their widespread presence in Massachusetts Bay throughout the year suggests this region may need to be managed to reduce the probability of collisions with ships and entanglement in fishing gear.","publication":"Conservation Biology","publication_with_fallback":"Conservation 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u-pv7x u-mb0x js-work-card work_48442173" data-work_id="48442173" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/48442173/Acoustic_monitoring_of_Atlantic_cod_Gadus_morhua_in_Massachusetts_Bay_implications_for_management_and_conservation">Acoustic monitoring of Atlantic cod (Gadus morhua) in Massachusetts Bay: implications for management and conservation</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Hernandez, K. M., Risch, D., Cholewiak, D. M., Dean, M. J., Hatch, L. T., Hoffman, W. S., Rice, A. N., Zemeckis, D., and Van Parijs, S. M. 2013. Acoustic monitoring of Atlantic cod (Gadus morhua) in Massachusetts Bay: implications for... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_48442173" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Hernandez, K. M., Risch, D., Cholewiak, D. M., Dean, M. J., Hatch, L. T., Hoffman, W. S., Rice, A. N., Zemeckis, D., and Van Parijs, S. M. 2013. Acoustic monitoring of Atlantic cod (Gadus morhua) in Massachusetts Bay: implications for management and conservation. – ICES Journal of Marine Science, 70: 628–635. Atlantic cod (Gadus morhua) stocks in northeastern US waters are depleted and stock recovery has been slow; research into the spawning behaviour of this species can help inform conservation and management measures. Male cod produce low-frequency grunts during courtship and spawning. Passive acoustic monitoring (PAM) offers a different perspective from which to investigate the occurrence, spatial extent and duration of spawning cod aggregations. A marine autonomous recording unit was deployed in the “Spring Cod Conservation Zone” (SCCZ) located in Massachusetts Bay, western Atlantic, to record cod grunts from April–June 2011. Cod grunts were present on 98.67% of the recording da...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/48442173" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="4b5555019824864cec043aa9965ea48d" rel="nofollow" data-download="{"attachment_id":67051543,"asset_id":48442173,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67051543/download_file?st=MTczOTkxNzc0NCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="85871" href="https://cornell.academia.edu/AaronRice">Aaron Rice</a><script data-card-contents-for-user="85871" type="text/json">{"id":85871,"first_name":"Aaron","last_name":"Rice","domain_name":"cornell","page_name":"AaronRice","display_name":"Aaron Rice","profile_url":"https://cornell.academia.edu/AaronRice","photo":"https://0.academia-photos.com/85871/2816415/11310789/s65_aaron.rice.png"}</script></span></span></li><li class="js-paper-rank-work_48442173 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="48442173"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 48442173, container: ".js-paper-rank-work_48442173", }); });</script></li><li class="js-percentile-work_48442173 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 48442173; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_48442173"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_48442173 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="48442173"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 48442173; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=48442173]").text(description); $(".js-view-count-work_48442173").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_48442173").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="48442173"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i></div><span class="InlineList-item-text u-textTruncate u-pl6x"><a class="InlineList-item-text" data-has-card-for-ri="28235" rel="nofollow" href="https://www.academia.edu/Documents/in/Multidisciplinary">Multidisciplinary</a><script data-card-contents-for-ri="28235" type="text/json">{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary","nofollow":true}</script></span></li><script>(function(){ if (false) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=48442173]'), work: {"id":48442173,"title":"Acoustic monitoring of Atlantic cod (Gadus morhua) in Massachusetts Bay: implications for management and conservation","created_at":"2021-05-04T18:48:27.201-07:00","owner_id":85871,"url":"https://www.academia.edu/48442173/Acoustic_monitoring_of_Atlantic_cod_Gadus_morhua_in_Massachusetts_Bay_implications_for_management_and_conservation","slug":"Acoustic_monitoring_of_Atlantic_cod_Gadus_morhua_in_Massachusetts_Bay_implications_for_management_and_conservation","dom_id":"work_48442173","summary":"Hernandez, K. M., Risch, D., Cholewiak, D. M., Dean, M. J., Hatch, L. T., Hoffman, W. S., Rice, A. N., Zemeckis, D., and Van Parijs, S. M. 2013. Acoustic monitoring of Atlantic cod (Gadus morhua) in Massachusetts Bay: implications for management and conservation. – ICES Journal of Marine Science, 70: 628–635. Atlantic cod (Gadus morhua) stocks in northeastern US waters are depleted and stock recovery has been slow; research into the spawning behaviour of this species can help inform conservation and management measures. Male cod produce low-frequency grunts during courtship and spawning. Passive acoustic monitoring (PAM) offers a different perspective from which to investigate the occurrence, spatial extent and duration of spawning cod aggregations. A marine autonomous recording unit was deployed in the “Spring Cod Conservation Zone” (SCCZ) located in Massachusetts Bay, western Atlantic, to record cod grunts from April–June 2011. Cod grunts were present on 98.67% of the recording da...","publication":"ICES Journal of Marine Science","publication_with_fallback":"ICES Journal of Marine Science","downloadable_attachments":[{"id":67051543,"asset_id":48442173,"asset_type":"Work","always_allow_download":false,"scribd_thumbnail_url":"https://attachments.academia-assets.com/67051543/thumbnails/1.jpg","download_url":"https://d1wqtxts1xzle7.cloudfront.net/67051543/fst003-libre.pdf?1620410278=\u0026response-content-disposition=attachment%3B+filename%3DAcoustic_monitoring_of_Atlantic_cod_Gadu.pdf\u0026Expires=1739921344\u0026Signature=KsvTHuzlS8l6svrZ9zZAaeIyEPIHP4t7tp1lNt2FaUASh1il3Em~o8zZWRffNiMXdRKeDkBzr4Lf7x8Buy~PI~3MXoxWvl0aOdWGwFtm0YnO-AyQq9PrVVK7qoanxjum1nPo0PdG-QHAsDf3PHXK1owzAapYP46y3ymh4vHrMe44UfcAd037yCtDot0kw6GjmuOksU91MEJof2~xItMVubk~n~GjuJniHdpqPz7IEHv8H~3IK2ZX~Bgfkd8Q~AcgOxGjKa2mFx-Q3T53AN7gu1wQWcQm2RhSHiu0VzvzULopXSSTRUuXvVoSe5jHkDd5EoWq6xQ7DHtu6onSwDu7xg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA","download_file_url":"https://www.academia.edu/attachments/67051543/download_file?st=MTczOTkxNzc0NCw4LjIyMi4yMDguMTQ2&","full_thumbnail_url":"https://0.academia-photos.com/attachment_thumbnails/67051543/mini_magick20210505-30838-yiopgg.png?1620261635"}],"downloadable_attachments_with_full_thumbnails":[{"id":67051543,"asset_id":48442173,"asset_type":"Work","always_allow_download":false,"scribd_thumbnail_url":"https://attachments.academia-assets.com/67051543/thumbnails/1.jpg","download_url":"https://d1wqtxts1xzle7.cloudfront.net/67051543/fst003-libre.pdf?1620410278=\u0026response-content-disposition=attachment%3B+filename%3DAcoustic_monitoring_of_Atlantic_cod_Gadu.pdf\u0026Expires=1739921344\u0026Signature=KsvTHuzlS8l6svrZ9zZAaeIyEPIHP4t7tp1lNt2FaUASh1il3Em~o8zZWRffNiMXdRKeDkBzr4Lf7x8Buy~PI~3MXoxWvl0aOdWGwFtm0YnO-AyQq9PrVVK7qoanxjum1nPo0PdG-QHAsDf3PHXK1owzAapYP46y3ymh4vHrMe44UfcAd037yCtDot0kw6GjmuOksU91MEJof2~xItMVubk~n~GjuJniHdpqPz7IEHv8H~3IK2ZX~Bgfkd8Q~AcgOxGjKa2mFx-Q3T53AN7gu1wQWcQm2RhSHiu0VzvzULopXSSTRUuXvVoSe5jHkDd5EoWq6xQ7DHtu6onSwDu7xg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA","download_file_url":"https://www.academia.edu/attachments/67051543/download_file?st=MTczOTkxNzc0NCw4LjIyMi4yMDguMTQ2&","full_thumbnail_url":"https://0.academia-photos.com/attachment_thumbnails/67051543/mini_magick20210505-30838-yiopgg.png?1620261635"}],"has_pdf":true,"has_fulltext":true,"page_count":8,"ordered_authors":[{"id":85871,"first_name":"Aaron","last_name":"Rice","domain_name":"cornell","page_name":"AaronRice","display_name":"Aaron Rice","profile_url":"https://cornell.academia.edu/AaronRice","photo":"https://0.academia-photos.com/85871/2816415/11310789/s65_aaron.rice.png"}],"research_interests":[{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary","nofollow":true}],"publication_year":2013,"publication_year_with_fallback":2013,"paper_rank":null,"all_time_views":6,"active_discussion":{}}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_48442174" data-work_id="48442174" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/48442174/Seasonal_and_geographical_patterns_of_fin_whale_song_in_the_western_North_Atlantic_Ocean">Seasonal and geographical patterns of fin whale song in the western North Atlantic Ocean</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Male fin whales, Balaenoptera physalus, produce a song consisting of 20 Hz notes at regularly spaced time intervals. Previous studies identified regional differences in fin whale internote intervals (INI), but seasonal changes within... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_48442174" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Male fin whales, Balaenoptera physalus, produce a song consisting of 20 Hz notes at regularly spaced time intervals. Previous studies identified regional differences in fin whale internote intervals (INI), but seasonal changes within populations have not been closely examined. To understand the patterns of fin whale song in the western North Atlantic, the seasonal abundance and acoustic features of fin whale song are measured from two years of archival passive acoustic recordings at two representative locations: Massachusetts Bay and New York Bight. Fin whale 20 Hz notes are detected on 99% of recorded days. In both regions, INI varies significantly throughout the year as two distinct periods: a "short-INI" season in September-January (9.6 s) and a "long-INI" season in March-May (15.1 s). February and June-August are transitional-INI months, with higher variability. Note abundance decreases with increasing INI, where note abundance is significantly lower in April-August than in September-January. Short-INI and high note abundance correspond to the fin whale reproductive season. The temporal variability of INI may be a mechanism by which fin whale individuals encode and communicate a variety of behaviorally relevant information.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/48442174" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9f05f391be661dec64ebf734895a6326" rel="nofollow" data-download="{"attachment_id":67051557,"asset_id":48442174,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67051557/download_file?st=MTczOTkxNzc0NCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="85871" href="https://cornell.academia.edu/AaronRice">Aaron Rice</a><script data-card-contents-for-user="85871" type="text/json">{"id":85871,"first_name":"Aaron","last_name":"Rice","domain_name":"cornell","page_name":"AaronRice","display_name":"Aaron Rice","profile_url":"https://cornell.academia.edu/AaronRice","photo":"https://0.academia-photos.com/85871/2816415/11310789/s65_aaron.rice.png"}</script></span></span></li><li class="js-paper-rank-work_48442174 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="48442174"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 48442174, container: ".js-paper-rank-work_48442174", }); 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$(".js-view-count[data-work-id=48442174]").text(description); $(".js-view-count-work_48442174").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_48442174").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="48442174"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">6</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="261" rel="nofollow" href="https://www.academia.edu/Documents/in/Geography">Geography</a>, <script data-card-contents-for-ri="261" type="text/json">{"id":261,"name":"Geography","url":"https://www.academia.edu/Documents/in/Geography","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="499" rel="nofollow" href="https://www.academia.edu/Documents/in/Acoustics">Acoustics</a>, <script data-card-contents-for-ri="499" type="text/json">{"id":499,"name":"Acoustics","url":"https://www.academia.edu/Documents/in/Acoustics","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="28235" rel="nofollow" href="https://www.academia.edu/Documents/in/Multidisciplinary">Multidisciplinary</a>, <script data-card-contents-for-ri="28235" type="text/json">{"id":28235,"name":"Multidisciplinary","url":"https://www.academia.edu/Documents/in/Multidisciplinary","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="93497" rel="nofollow" href="https://www.academia.edu/Documents/in/Atlantic_Ocean">Atlantic Ocean</a><script data-card-contents-for-ri="93497" type="text/json">{"id":93497,"name":"Atlantic Ocean","url":"https://www.academia.edu/Documents/in/Atlantic_Ocean","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=48442174]'), work: {"id":48442174,"title":"Seasonal and geographical patterns of fin whale song in the western North Atlantic Ocean","created_at":"2021-05-04T18:48:27.274-07:00","owner_id":85871,"url":"https://www.academia.edu/48442174/Seasonal_and_geographical_patterns_of_fin_whale_song_in_the_western_North_Atlantic_Ocean","slug":"Seasonal_and_geographical_patterns_of_fin_whale_song_in_the_western_North_Atlantic_Ocean","dom_id":"work_48442174","summary":"Male fin whales, Balaenoptera physalus, produce a song consisting of 20 Hz notes at regularly spaced time intervals. 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I feel tiny, swallowed up by the immense deep blue that envelops me. To my right, I see my dive buddy, Aaron Rice, collecting survey data. A moving kaleidoscope of corals, sponges and fishes... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_48442197" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">My depth gauge reads 15 meters (about 45 feet). I feel tiny, swallowed up by the immense deep blue that envelops me. To my right, I see my dive buddy, Aaron Rice, collecting survey data. A moving kaleidoscope of corals, sponges and fishes bedazzles me with its iridescent colors, fantastic shapes and captivating action. 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class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="85871" href="https://cornell.academia.edu/AaronRice">Aaron Rice</a><script data-card-contents-for-user="85871" type="text/json">{"id":85871,"first_name":"Aaron","last_name":"Rice","domain_name":"cornell","page_name":"AaronRice","display_name":"Aaron Rice","profile_url":"https://cornell.academia.edu/AaronRice","photo":"https://0.academia-photos.com/85871/2816415/11310789/s65_aaron.rice.png"}</script></span></span></li><li class="js-paper-rank-work_48442203 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="48442203"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 48442203, container: ".js-paper-rank-work_48442203", }); });</script></li><li class="js-percentile-work_48442203 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 48442203; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_48442203"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_48442203 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="48442203"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 48442203; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=48442203]").text(description); $(".js-view-count-work_48442203").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_48442203").removeClass('hidden') })</script></div></li></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_48442204" data-work_id="48442204" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/48442204/Carnival_of_coral_reef_fishes">Carnival of coral reef fishes</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">My depth gauge reads 15 meters (about 45 feet). I feel tiny, swallowed up by the immense deep blue that envelops me. To my right, I see my dive buddy, Aaron Rice, collecting survey data. A moving kaleidoscope of corals, sponges and fishes... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_48442204" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">My depth gauge reads 15 meters (about 45 feet). I feel tiny, swallowed up by the immense deep blue that envelops me. To my right, I see my dive buddy, Aaron Rice, collecting survey data. A moving kaleidoscope of corals, sponges and fishes bedazzles me with its iridescent colors, fantastic shapes and captivating action. Up to this point, our survey of coral reef fishes had been relatively normal, until a looming shadow catches my eye.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/48442204" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="d92a9327ad5ce811f8b12a37ec86226c" rel="nofollow" data-download="{"attachment_id":67051564,"asset_id":48442204,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67051564/download_file?st=MTczOTkxNzc0NCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="85871" href="https://cornell.academia.edu/AaronRice">Aaron Rice</a><script data-card-contents-for-user="85871" type="text/json">{"id":85871,"first_name":"Aaron","last_name":"Rice","domain_name":"cornell","page_name":"AaronRice","display_name":"Aaron Rice","profile_url":"https://cornell.academia.edu/AaronRice","photo":"https://0.academia-photos.com/85871/2816415/11310789/s65_aaron.rice.png"}</script></span></span></li><li class="js-paper-rank-work_48442204 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="48442204"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 48442204, container: ".js-paper-rank-work_48442204", }); });</script></li><li class="js-percentile-work_48442204 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 48442204; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_48442204"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_48442204 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="48442204"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 48442204; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=48442204]").text(description); $(".js-view-count-work_48442204").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_48442204").removeClass('hidden') })</script></div></li></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_48442211" data-work_id="48442211" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/48442211/The_evolution_of_jaw_protrusion_mechanics_has_been_tightly_coupled_to_bentho_pelagic_divergence_in_damselfishes_Pomacentridae_">The evolution of jaw protrusion mechanics has been tightly coupled to bentho-pelagic divergence in damselfishes (Pomacentridae)</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Most species-rich lineages of aquatic organisms have undergone divergence between forms that feed from the substrate (benthic feeding) and forms that feed from the water column (pelagic feeding). Changes in trophic niche are frequently... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_48442211" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Most species-rich lineages of aquatic organisms have undergone divergence between forms that feed from the substrate (benthic feeding) and forms that feed from the water column (pelagic feeding). Changes in trophic niche are frequently accompanied by changes in skull mechanics, and multiple fish lineages have evolved highly specialized biomechanical configurations that allow them to protrude their upper jaws toward the prey during feeding. Damselfishes (family Pomacentridae) are an example of a species-rich lineage with multiple trophic morphologies and feeding ecologies. We sought to determine if bentho-pelagic divergence in the damselfishes has been tightly coupled to changes in jaw protrusion ability. Using high-speed video recordings and kinematic analysis we examined feeding performance in ten species that include three examples of convergence on herbivory, three examples of convergence on omnivory and two examples of convergence on planktivory. We also utilized morphometrics t...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/48442211" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="a96ceccebe1a16272f5f430b3db4f33c" rel="nofollow" data-download="{"attachment_id":67051551,"asset_id":48442211,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67051551/download_file?st=MTczOTkxNzc0NCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="85871" href="https://cornell.academia.edu/AaronRice">Aaron Rice</a><script data-card-contents-for-user="85871" type="text/json">{"id":85871,"first_name":"Aaron","last_name":"Rice","domain_name":"cornell","page_name":"AaronRice","display_name":"Aaron Rice","profile_url":"https://cornell.academia.edu/AaronRice","photo":"https://0.academia-photos.com/85871/2816415/11310789/s65_aaron.rice.png"}</script></span></span></li><li class="js-paper-rank-work_48442211 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="48442211"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 48442211, container: ".js-paper-rank-work_48442211", }); 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$(".js-view-count[data-work-id=48442211]").text(description); $(".js-view-count-work_48442211").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_48442211").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="48442211"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="47884" rel="nofollow" href="https://www.academia.edu/Documents/in/Biological_Sciences">Biological Sciences</a>, <script data-card-contents-for-ri="47884" type="text/json">{"id":47884,"name":"Biological Sciences","url":"https://www.academia.edu/Documents/in/Biological_Sciences","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="3763225" rel="nofollow" href="https://www.academia.edu/Documents/in/Medical_and_Health_Sciences">Medical and Health Sciences</a><script data-card-contents-for-ri="3763225" type="text/json">{"id":3763225,"name":"Medical and Health Sciences","url":"https://www.academia.edu/Documents/in/Medical_and_Health_Sciences","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=48442211]'), work: {"id":48442211,"title":"The evolution of jaw protrusion mechanics has been tightly coupled to bentho-pelagic divergence in damselfishes (Pomacentridae)","created_at":"2021-05-04T18:48:29.141-07:00","owner_id":85871,"url":"https://www.academia.edu/48442211/The_evolution_of_jaw_protrusion_mechanics_has_been_tightly_coupled_to_bentho_pelagic_divergence_in_damselfishes_Pomacentridae_","slug":"The_evolution_of_jaw_protrusion_mechanics_has_been_tightly_coupled_to_bentho_pelagic_divergence_in_damselfishes_Pomacentridae_","dom_id":"work_48442211","summary":"Most species-rich lineages of aquatic organisms have undergone divergence between forms that feed from the substrate (benthic feeding) and forms that feed from the water column (pelagic feeding). Changes in trophic niche are frequently accompanied by changes in skull mechanics, and multiple fish lineages have evolved highly specialized biomechanical configurations that allow them to protrude their upper jaws toward the prey during feeding. Damselfishes (family Pomacentridae) are an example of a species-rich lineage with multiple trophic morphologies and feeding ecologies. We sought to determine if bentho-pelagic divergence in the damselfishes has been tightly coupled to changes in jaw protrusion ability. Using high-speed video recordings and kinematic analysis we examined feeding performance in ten species that include three examples of convergence on herbivory, three examples of convergence on omnivory and two examples of convergence on planktivory. 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To understand how the construction of an offshore windfarm in the Maryland Wind Energy Area (WEA) off Maryland, USA,... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_48442213" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Offshore windfarms provide renewable energy, but activities during the construction phase can affect marine mammals. To understand how the construction of an offshore windfarm in the Maryland Wind Energy Area (WEA) off Maryland, USA, might impact harbour porpoises (Phocoena phocoena), it is essential to determine their poorly understood year-round distribution. Although habitat-based models can help predict the occurrence of species in areas with limited or no sampling, they require validation to determine the accuracy of the predictions. Incorporating more than 18 months of harbour porpoise detection data from passive acoustic monitoring, generalized auto-regressive moving average and generalized additive models were used to investigate harbour porpoise occurrence within and around the Maryland WEA in relation to temporal and environmental variables. Acoustic detection metrics were compared to habitat-based density estimates derived from aerial and boat-based sightings to validate ...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/48442213" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="2d4740f0fbc072ad2f6053ea3f351150" rel="nofollow" data-download="{"attachment_id":67051554,"asset_id":48442213,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67051554/download_file?st=MTczOTkxNzc0NCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="85871" href="https://cornell.academia.edu/AaronRice">Aaron Rice</a><script data-card-contents-for-user="85871" type="text/json">{"id":85871,"first_name":"Aaron","last_name":"Rice","domain_name":"cornell","page_name":"AaronRice","display_name":"Aaron Rice","profile_url":"https://cornell.academia.edu/AaronRice","photo":"https://0.academia-photos.com/85871/2816415/11310789/s65_aaron.rice.png"}</script></span></span></li><li class="js-paper-rank-work_48442213 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="48442213"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 48442213, container: ".js-paper-rank-work_48442213", }); 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Eubalaena glacialis) population in recent years, an improved understanding of spatio-temporal movements are imperative for the conservation of this... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_48442216" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Given new distribution patterns of the endangered North Atlantic right whale (NARW; Eubalaena glacialis) population in recent years, an improved understanding of spatio-temporal movements are imperative for the conservation of this species. While so far visual data have provided most information on NARW movements, passive acoustic monitoring (PAM) was used in this study in order to better capture year-round NARW presence. This project used PAM data from 2004 to 2014 collected by 19 organizations throughout the western North Atlantic Ocean. Overall, data from 324 recorders (35,600 days) were processed and analyzed using a classification and detection system. Results highlight almost year-round habitat use of the western North Atlantic Ocean, with a decrease in detections in waters off Cape Hatteras, North Carolina in summer and fall. Data collected post 2010 showed an increased NARW presence in the mid-Atlantic region and a simultaneous decrease in the northern Gulf of Maine. In addi...</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/48442216" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="8f491607e0a4a2b0fe3be4e0d2219d72" rel="nofollow" data-download="{"attachment_id":67051561,"asset_id":48442216,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/67051561/download_file?st=MTczOTkxNzc0NCw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="85871" href="https://cornell.academia.edu/AaronRice">Aaron Rice</a><script data-card-contents-for-user="85871" type="text/json">{"id":85871,"first_name":"Aaron","last_name":"Rice","domain_name":"cornell","page_name":"AaronRice","display_name":"Aaron Rice","profile_url":"https://cornell.academia.edu/AaronRice","photo":"https://0.academia-photos.com/85871/2816415/11310789/s65_aaron.rice.png"}</script></span></span></li><li class="js-paper-rank-work_48442216 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="48442216"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 48442216, container: ".js-paper-rank-work_48442216", }); 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Eubalaena glacialis) population in recent years, an improved understanding of spatio-temporal movements are imperative for the conservation of this species. While so far visual data have provided most information on NARW movements, passive acoustic monitoring (PAM) was used in this study in order to better capture year-round NARW presence. This project used PAM data from 2004 to 2014 collected by 19 organizations throughout the western North Atlantic Ocean. Overall, data from 324 recorders (35,600 days) were processed and analyzed using a classification and detection system. Results highlight almost year-round habitat use of the western North Atlantic Ocean, with a decrease in detections in waters off Cape Hatteras, North Carolina in summer and fall. Data collected post 2010 showed an increased NARW presence in the mid-Atlantic region and a simultaneous decrease in the northern Gulf of Maine. 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We apply a continuous region... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_30274946" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper, we develop a novel method based on machine-learning and image processing to identify North Atlantic right whale (NARW) upcalls in the presence of high levels of ambient and interfering noise. We apply a continuous region algorithm on the spectrogram to extract the regions of interest, and then use grid masking techniques to generate a small feature set that is then used in an artificial neural network classifier to identify the NARW up-calls. It is shown that the proposed technique is effective in detecting and capturing even very faint up-calls, in the presence of ambient and interfering noises. The method is evaluated on a dataset recorded in Massachusetts Bay, United States. The dataset includes 20000 sound clips for training, and 10000 sound clips for testing. The results show that the proposed technique can achieve an error rate of less than FPR = 4.5% for a 90% true positive rate.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/30274946" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="c70a12ce2a4f326866ee9b3a0a63735a" rel="nofollow" data-download="{"attachment_id":50741145,"asset_id":30274946,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50741145/download_file?st=MTczOTkxNzc0NSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="57558306" href="https://cornell.academia.edu/PDugan">P. 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The method presented herein exploits an artificial neural... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_30274947" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper, we propose a method to improve sound classification performance by combining signal features, derived from the time-frequency spectrogram, with human perception. The method presented herein exploits an artificial neural network (ANN) and learns the signal features based on the human perception knowledge. The proposed method is applied to a large acoustic dataset containing 24 months of nearly continuous recordings. The results show a significant improvement in performance of the detection-classification system; yielding as much as 20% improvement in true positive rate for a given false positive rate.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/30274947" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b5398e49505c64cb543a22ba22224273" rel="nofollow" data-download="{"attachment_id":50741143,"asset_id":30274947,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/50741143/download_file?st=MTczOTkxNzc0NSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="57558306" href="https://cornell.academia.edu/PDugan">P. 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The method presented herein exploits an artificial neural network (ANN) and learns the signal features based on the human perception knowledge. The proposed method is applied to a large acoustic dataset containing 24 months of nearly continuous recordings. 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The exploration for fossil-fuel or alternative energy and the construction of facilities to support these... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_30916355" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">— The possible effects of anthropogenic noise on the marine environment is becoming an important topic in the oceanic community. The exploration for fossil-fuel or alternative energy and the construction of facilities to support these endeavors often requires sizable construction efforts; which usually require permitting to study the impact of noise on the environment. Of particular interest is the variety of data products used to influence environmental impact reports and the processing time required to generate these data from large amounts of passive acoustic recordings. This paper outlines work being done by the Bioacoustics Research Program at Cornell University and the Lab of Ornithology, (BRP) for developing MATLAB tools in support of environmental compliance reporting. Due to the success of acoustic monitoring, understanding acoustic signatures is now becoming part of environmental impact assessment and required compliance for permitting. BRP has leveraged various existing tools and capabilities which result in integrated special purpose software tools within a MATLAB toolbox called SEDNA 1. SEDNA incorporates various tools to measure acute and chronic noise levels, detect and classify marine mammal vocalizations, and compute various metrics such as receive levels, signal excess, masking and communication space. This work will summarize the high performance computing strategy used in the SEDNA Toolbox along with the capability to integrate various layers of data within a modeling framework that incorporates ambient noise, vessel and animal data. Finally, the work will demonstrate the power of this approach through animated data visualization, showing animal, vessel and ambient noise integrated over relatively large temporal and spatial scales. 1 In Inuit mythology, Sedna (Inuktitut Sanna) is the goddess of the sea and marine animals. <a href="http://en.wikipedia.org/wiki/Sedna_(mythology)" rel="nofollow">http://en.wikipedia.org/wiki/Sedna_(mythology)</a>.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/30916355" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="f2a105beb9b14f94c579db8e31489181" rel="nofollow" data-download="{"attachment_id":51340288,"asset_id":30916355,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/51340288/download_file?st=MTczOTkxNzc0NSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="25601845" href="https://independent.academia.edu/AnnWarde">Ann Warde</a><script data-card-contents-for-user="25601845" type="text/json">{"id":25601845,"first_name":"Ann","last_name":"Warde","domain_name":"independent","page_name":"AnnWarde","display_name":"Ann Warde","profile_url":"https://independent.academia.edu/AnnWarde","photo":"https://0.academia-photos.com/25601845/10589212/15903485/s65_ann.warde.jpg"}</script></span></span><span class="u-displayInlineBlock InlineList-item-text"> and <span class="u-textDecorationUnderline u-clickable InlineList-item-text js-work-more-authors-30916355">+2</span><div class="hidden js-additional-users-30916355"><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://independent.academia.edu/CClark5">C. Clark</a></span></div><div><span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a href="https://cornell.academia.edu/PDugan">P. 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Comparison of Machine Learning Recognition Algorithms</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper compares three different approaches currently used in recognizing contact calls made from the North Atlantic Right Whale (NRW), Eubalaena glacialis. We present two new approaches consisting of machine learning algorithms based... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_116311186" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper compares three different approaches currently used in recognizing contact calls made from the North Atlantic Right Whale (NRW), Eubalaena glacialis. We present two new approaches consisting of machine learning algorithms based on artificial neural networks (NET) and the classification and regression tree classifiers (CART), and compare their performance with earlier work that employs multi-Stage feature vector testing (FVT) approach. A combined total of over 100,000 noise and NRW up-call events were used in the study. Calls were primarily recorded from two areas, Cape Cod Bay and Great South Channel. Of the three classifiers, the CART had the highest assignment rates, overall 86.45% with highest false positive rates (<100 per hour). The FVT Method had exceptionally low false positive rates, with <50 per hour. However, it had an overall assignment rate less than the NET. The CART had statistically the same false positive rate as the NET with the highest assignment rates, 2.2% higher than the NET and 11.75% greater than the FVT Method. Details of the results are shown and extensions to the research are discussed.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/116311186" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="10b4f69b1eb07137a3dc52bead60e782" rel="nofollow" data-download="{"attachment_id":112478003,"asset_id":116311186,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/112478003/download_file?st=MTczOTkxNzc0NSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="57558306" href="https://cornell.academia.edu/PDugan">P. 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Calls were primarily recorded from two areas, Cape Cod Bay and Great South Channel. Of the three classifiers, the CART had the highest assignment rates, overall 86.45% with highest false positive rates (\u003c100 per hour). The FVT Method had exceptionally low false positive rates, with \u003c50 per hour. However, it had an overall assignment rate less than the NET. The CART had statistically the same false positive rate as the NET with the highest assignment rates, 2.2% higher than the NET and 11.75% greater than the FVT Method. Details of the results are shown and extensions to the research are discussed.","publication":"IEEE","publication_with_fallback":"IEEE","downloadable_attachments":[{"id":112478003,"asset_id":116311186,"asset_type":"Work","always_allow_download":false,"scribd_thumbnail_url":"https://attachments.academia-assets.com/112478003/thumbnails/1.jpg","download_url":"https://d1wqtxts1xzle7.cloudfront.net/112478003/North_Atlantic_Right_Whale_acoustic_sign-libre.pdf?1710626995=\u0026response-content-disposition=attachment%3B+filename%3DNorth_Atlantic_Right_Whale_Acoustic_Sign.pdf\u0026Expires=1739921345\u0026Signature=TZ26diyY3STS-qelZcRLdi4jgcw93NVe6GaR54gf2oVikwIlv-UoHAUVA6Sw4Egc811fFG3KhBSDI-4crVELVTsqfg6JXpNZwUJlmSAqWDQ2HMK4huYYTUj52cWRnzNOyYn9hTe5R0frIfIPPZ~FV8Fnk5RnVKglgtaPJihrSN7qqSzGFqOjpS5htuttCdgVkTjxZ~ZIcS5S~FB8dLng1M6uLJLTUFXyMn8hhxqz0BnsF23AURVgXeQXnmEa37scKn0thJasFbSdpnIlNmACzpgUbjN8nlzXGarhmufZ51scDBsFv83TOHyb5Yrnyl4zRvR5ZaqLc~H2yyB4qJjaNg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA","download_file_url":"https://www.academia.edu/attachments/112478003/download_file?st=MTczOTkxNzc0NSw4LjIyMi4yMDguMTQ2&","full_thumbnail_url":"https://0.academia-photos.com/attachment_thumbnails/112478003/mini_magick20240316-1-3k7el2.png?1710623702"}],"downloadable_attachments_with_full_thumbnails":[{"id":112478003,"asset_id":116311186,"asset_type":"Work","always_allow_download":false,"scribd_thumbnail_url":"https://attachments.academia-assets.com/112478003/thumbnails/1.jpg","download_url":"https://d1wqtxts1xzle7.cloudfront.net/112478003/North_Atlantic_Right_Whale_acoustic_sign-libre.pdf?1710626995=\u0026response-content-disposition=attachment%3B+filename%3DNorth_Atlantic_Right_Whale_Acoustic_Sign.pdf\u0026Expires=1739921345\u0026Signature=TZ26diyY3STS-qelZcRLdi4jgcw93NVe6GaR54gf2oVikwIlv-UoHAUVA6Sw4Egc811fFG3KhBSDI-4crVELVTsqfg6JXpNZwUJlmSAqWDQ2HMK4huYYTUj52cWRnzNOyYn9hTe5R0frIfIPPZ~FV8Fnk5RnVKglgtaPJihrSN7qqSzGFqOjpS5htuttCdgVkTjxZ~ZIcS5S~FB8dLng1M6uLJLTUFXyMn8hhxqz0BnsF23AURVgXeQXnmEa37scKn0thJasFbSdpnIlNmACzpgUbjN8nlzXGarhmufZ51scDBsFv83TOHyb5Yrnyl4zRvR5ZaqLc~H2yyB4qJjaNg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA","download_file_url":"https://www.academia.edu/attachments/112478003/download_file?st=MTczOTkxNzc0NSw4LjIyMi4yMDguMTQ2&","full_thumbnail_url":"https://0.academia-photos.com/attachment_thumbnails/112478003/mini_magick20240316-1-3k7el2.png?1710623702"}],"has_pdf":true,"has_fulltext":true,"page_count":6,"ordered_authors":[{"id":57558306,"first_name":"P.","last_name":"Dugan","domain_name":"cornell","page_name":"PDugan","display_name":"P. Dugan","profile_url":"https://cornell.academia.edu/PDugan","photo":"https://0.academia-photos.com/57558306/15132782/15834168/s65_p..dugan.jpg"}],"research_interests":[{"id":54123,"name":"Artificial Neural Networks","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Networks","nofollow":true},{"id":143038,"name":"Machine Learning and Pattern Recognition","url":"https://www.academia.edu/Documents/in/Machine_Learning_and_Pattern_Recognition","nofollow":true},{"id":176822,"name":"Marine Bioacoustics","url":"https://www.academia.edu/Documents/in/Marine_Bioacoustics","nofollow":true},{"id":561323,"name":"Applied Machine Learning","url":"https://www.academia.edu/Documents/in/Applied_Machine_Learning","nofollow":true},{"id":1185455,"name":"Southern Right Whales","url":"https://www.academia.edu/Documents/in/Southern_Right_Whales"},{"id":1433808,"name":"Convolutional Neural Networks","url":"https://www.academia.edu/Documents/in/Convolutional_Neural_Networks"},{"id":2548020,"name":"Automatic Detection","url":"https://www.academia.edu/Documents/in/Automatic_Detection"},{"id":2671149,"name":"North Atlantic right whales","url":"https://www.academia.edu/Documents/in/North_Atlantic_right_whales"}],"publication_year":2010,"publication_year_with_fallback":2010,"paper_rank":null,"all_time_views":3,"active_discussion":{}}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_57846721" data-work_id="57846721" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/57846721/Minke_whale_acoustic_behavior_and_multi_year_seasonal_and_diel_vocalization_patterns_in_Massachusetts_Bay_USA">Minke whale acoustic behavior and multi-year seasonal and diel vocalization patterns in Massachusetts Bay, USA</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Passive acoustic monitoring (PAM) is a rapidly growing field, providing valuable insights in marine ecology. The approach allows for long-term, species-specific monitoring over a range of spatial scales. For many baleen whales fundamental... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_57846721" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Passive acoustic monitoring (PAM) is a rapidly growing field, providing valuable insights in marine ecology. The approach allows for long-term, species-specific monitoring over a range of spatial scales. For many baleen whales fundamental information on seasonal occurrence and distribution is still missing. In this study, pulse trains produced by the North Atlantic minke whale, a highly mobile and cryptic species, are used to examine its seasonality, diel vocalization patterns and spatial distribution throughout the Stellwagen Bank National Marine Sanctuary (SBNMS), USA. Three and a half years (2006, 2007 to 2010) of near continuous passive acoustic data were analyzed using automated detection methods. Random forests and cluster analyses grouped pulse trains into 3 main categories (slow-down, constant and speed-up), with several subtypes. Slowdown pulse trains were the most commonly recorded call category. Minke whale pulse train occurrence was highly seasonal across all years. Detections were made from August to November, with 88% occurring in September and October. No detections were recorded in January and February, and only few from March to June. Minke whale pulse trains showed a distinct diel pattern, with a nighttime peak from approximately 20:00 to 01:00 h Eastern Standard Time (EST). The highest numbers of pulse trains were detected to the east of Stellwagen Bank, suggesting that minke whales travel preferably in deeper waters along the outer edge of the sanctuary. These data show that minke whales consistently use Stellwagen Bank as part of their migration route to and from the feeding grounds. Unlike other baleen whales in this area they do not appear to have a persistent year-round acoustic presence.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/57846721" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="7bd6905d565db27083cbcf8d78dc8c4a" rel="nofollow" data-download="{"attachment_id":72545737,"asset_id":57846721,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/72545737/download_file?st=MTczOTkxNzc0NSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="156002595" href="https://cornell.academia.edu/PeterDugan">Peter Dugan</a><script data-card-contents-for-user="156002595" type="text/json">{"id":156002595,"first_name":"Peter","last_name":"Dugan","domain_name":"cornell","page_name":"PeterDugan","display_name":"Peter Dugan","profile_url":"https://cornell.academia.edu/PeterDugan","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_57846721 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="57846721"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 57846721, container: ".js-paper-rank-work_57846721", }); 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$(".js-view-count[data-work-id=57846721]").text(description); $(".js-view-count-work_57846721").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_57846721").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="57846721"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">2</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="173" rel="nofollow" href="https://www.academia.edu/Documents/in/Zoology">Zoology</a>, <script data-card-contents-for-ri="173" type="text/json">{"id":173,"name":"Zoology","url":"https://www.academia.edu/Documents/in/Zoology","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="9846" rel="nofollow" href="https://www.academia.edu/Documents/in/Ecology">Ecology</a><script data-card-contents-for-ri="9846" type="text/json">{"id":9846,"name":"Ecology","url":"https://www.academia.edu/Documents/in/Ecology","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=57846721]'), work: {"id":57846721,"title":"Minke whale acoustic behavior and multi-year seasonal and diel vocalization patterns in Massachusetts Bay, USA","created_at":"2021-10-14T09:11:34.782-07:00","owner_id":156002595,"url":"https://www.academia.edu/57846721/Minke_whale_acoustic_behavior_and_multi_year_seasonal_and_diel_vocalization_patterns_in_Massachusetts_Bay_USA","slug":"Minke_whale_acoustic_behavior_and_multi_year_seasonal_and_diel_vocalization_patterns_in_Massachusetts_Bay_USA","dom_id":"work_57846721","summary":"Passive acoustic monitoring (PAM) is a rapidly growing field, providing valuable insights in marine ecology. The approach allows for long-term, species-specific monitoring over a range of spatial scales. For many baleen whales fundamental information on seasonal occurrence and distribution is still missing. In this study, pulse trains produced by the North Atlantic minke whale, a highly mobile and cryptic species, are used to examine its seasonality, diel vocalization patterns and spatial distribution throughout the Stellwagen Bank National Marine Sanctuary (SBNMS), USA. Three and a half years (2006, 2007 to 2010) of near continuous passive acoustic data were analyzed using automated detection methods. Random forests and cluster analyses grouped pulse trains into 3 main categories (slow-down, constant and speed-up), with several subtypes. Slowdown pulse trains were the most commonly recorded call category. Minke whale pulse train occurrence was highly seasonal across all years. Detections were made from August to November, with 88% occurring in September and October. No detections were recorded in January and February, and only few from March to June. Minke whale pulse trains showed a distinct diel pattern, with a nighttime peak from approximately 20:00 to 01:00 h Eastern Standard Time (EST). The highest numbers of pulse trains were detected to the east of Stellwagen Bank, suggesting that minke whales travel preferably in deeper waters along the outer edge of the sanctuary. These data show that minke whales consistently use Stellwagen Bank as part of their migration route to and from the feeding grounds. Unlike other baleen whales in this area they do not appear to have a persistent year-round acoustic presence.","publication":"Marine Ecology Progress Series","publication_with_fallback":"Marine Ecology Progress Series","downloadable_attachments":[{"id":72545737,"asset_id":57846721,"asset_type":"Work","always_allow_download":false,"scribd_thumbnail_url":"https://attachments.academia-assets.com/72545737/thumbnails/1.jpg","download_url":"https://d1wqtxts1xzle7.cloudfront.net/72545737/Minke_whale_acoustic_behavior_and_multi-20211014-2092-h2bqz1.pdf?1738439414=\u0026response-content-disposition=attachment%3B+filename%3DMinke_whale_acoustic_behavior_and_multi.pdf\u0026Expires=1739921345\u0026Signature=T-dmdpMyxWoOwYF24xPRKj7yOnAEd55YX90Zcv9jJowCe8emeAj0JNwAEI9iMxNlBW2B-YVnVwlWeI0DspJz36mt1jg1yXm~1HdNFLjDlfOKvT8w5QHkX5s7in8UqBbMNdmRjA-LD2jxO~hWRfdNlwy0UFKbas9Ku0xBikFR8sD5mXZdMlCjs5qQMij9k9EgDexcVn~0~TYrN5XCBa~cTM0BKhU8TU-zpxEhK0TjnW3ZRGjRlzSRh0PE5aaZzk~NhJqMKqIA3bZ4OvUlTnHuhaOkm~TmWdjq6JGVM6I9citXQ-xRNvHJVt40D70zXtXRNLDbXwPOqlZzIq86rj29yg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA","download_file_url":"https://www.academia.edu/attachments/72545737/download_file?st=MTczOTkxNzc0NSw4LjIyMi4yMDguMTQ2&","full_thumbnail_url":"https://0.academia-photos.com/attachment_thumbnails/72545737/mini_magick20211014-9931-1yt75nw.png?1634232287"}],"downloadable_attachments_with_full_thumbnails":[{"id":72545737,"asset_id":57846721,"asset_type":"Work","always_allow_download":false,"scribd_thumbnail_url":"https://attachments.academia-assets.com/72545737/thumbnails/1.jpg","download_url":"https://d1wqtxts1xzle7.cloudfront.net/72545737/Minke_whale_acoustic_behavior_and_multi-20211014-2092-h2bqz1.pdf?1738439414=\u0026response-content-disposition=attachment%3B+filename%3DMinke_whale_acoustic_behavior_and_multi.pdf\u0026Expires=1739921345\u0026Signature=T-dmdpMyxWoOwYF24xPRKj7yOnAEd55YX90Zcv9jJowCe8emeAj0JNwAEI9iMxNlBW2B-YVnVwlWeI0DspJz36mt1jg1yXm~1HdNFLjDlfOKvT8w5QHkX5s7in8UqBbMNdmRjA-LD2jxO~hWRfdNlwy0UFKbas9Ku0xBikFR8sD5mXZdMlCjs5qQMij9k9EgDexcVn~0~TYrN5XCBa~cTM0BKhU8TU-zpxEhK0TjnW3ZRGjRlzSRh0PE5aaZzk~NhJqMKqIA3bZ4OvUlTnHuhaOkm~TmWdjq6JGVM6I9citXQ-xRNvHJVt40D70zXtXRNLDbXwPOqlZzIq86rj29yg__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA","download_file_url":"https://www.academia.edu/attachments/72545737/download_file?st=MTczOTkxNzc0NSw4LjIyMi4yMDguMTQ2&","full_thumbnail_url":"https://0.academia-photos.com/attachment_thumbnails/72545737/mini_magick20211014-9931-1yt75nw.png?1634232287"}],"has_pdf":true,"has_fulltext":true,"page_count":17,"ordered_authors":[{"id":156002595,"first_name":"Peter","last_name":"Dugan","domain_name":"cornell","page_name":"PeterDugan","display_name":"Peter Dugan","profile_url":"https://cornell.academia.edu/PeterDugan","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":173,"name":"Zoology","url":"https://www.academia.edu/Documents/in/Zoology","nofollow":true},{"id":9846,"name":"Ecology","url":"https://www.academia.edu/Documents/in/Ecology","nofollow":true}],"publication_year":2013,"publication_year_with_fallback":2013,"paper_rank":null,"all_time_views":12,"active_discussion":{}}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_57846723" data-work_id="57846723" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/57846723/Seasonal_migrations_of_North_Atlantic_minke_whales_novel_insights_from_large_scale_passive_acoustic_monitoring_networks">Seasonal migrations of North Atlantic minke whales: novel insights from large-scale passive acoustic monitoring networks</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">Background: Little is known about migration patterns and seasonal distribution away from coastal summer feeding habitats of many pelagic baleen whales. Recently, large-scale passive acoustic monitoring networks have become available to... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_57846723" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">Background: Little is known about migration patterns and seasonal distribution away from coastal summer feeding habitats of many pelagic baleen whales. Recently, large-scale passive acoustic monitoring networks have become available to explore migration patterns and identify critical habitats of these species. North Atlantic minke whales (Balaenoptera acutorostrata) perform seasonal migrations between high latitude summer feeding and low latitude winter breeding grounds. While the distribution and abundance of the species has been studied across their summer range, data on migration and winter habitat are virtually missing. Acoustic recordings, from 16 different sites from across the North Atlantic, were analyzed to examine the seasonal and geographic variation in minke whale pulse train occurrence, infer information about migration routes and timing, and to identify possible winter habitats. Results: Acoustic detections show that minke whales leave their winter grounds south of 30°N from March through early April. On their southward migration in autumn, minke whales leave waters north of 40°N from mid-October through early November. In the western North Atlantic spring migrants appear to track the warmer waters of the Gulf Stream along the continental shelf, while whales travel farther offshore in autumn. Abundant detections were found off the southeastern US and the Caribbean during winter. Minke whale pulse trains showed evidence of geographic variation, with longer pulse trains recorded south of 40°N. Very few pulse trains were recorded during summer in any of the datasets. Conclusion: This study highlights the feasibility of using acoustic monitoring networks to explore migration patterns of pelagic marine mammals. Results confirm the presence of minke whales off the southeastern US and the Caribbean during winter months. The absence of pulse train detections during summer suggests either that minke whales switch their vocal behaviour at this time of year, are absent from available recording sites or that variation in signal structure influenced automated detection. Alternatively, if pulse trains are produced in a reproductive context by males, these data may indicate their absence from the selected recording sites. Evidence of geographic variation in pulse train duration suggests different behavioural functions or use of these calls at different latitudes.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/57846723" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="841ff48e6b3f60e269382a72ef2c9270" rel="nofollow" data-download="{"attachment_id":72545736,"asset_id":57846723,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/72545736/download_file?st=MTczOTkxNzc0NSw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="156002595" href="https://cornell.academia.edu/PeterDugan">Peter Dugan</a><script data-card-contents-for-user="156002595" type="text/json">{"id":156002595,"first_name":"Peter","last_name":"Dugan","domain_name":"cornell","page_name":"PeterDugan","display_name":"Peter Dugan","profile_url":"https://cornell.academia.edu/PeterDugan","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_57846723 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="57846723"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 57846723, container: ".js-paper-rank-work_57846723", }); 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$(".js-view-count[data-work-id=57846723]").text(description); $(".js-view-count-work_57846723").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_57846723").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="57846723"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i></div><span class="InlineList-item-text u-textTruncate u-pl6x"><a class="InlineList-item-text" data-has-card-for-ri="2982" rel="nofollow" href="https://www.academia.edu/Documents/in/Movement_Ecology">Movement Ecology</a><script data-card-contents-for-ri="2982" type="text/json">{"id":2982,"name":"Movement Ecology","url":"https://www.academia.edu/Documents/in/Movement_Ecology","nofollow":true}</script></span></li><script>(function(){ if (false) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=57846723]'), work: {"id":57846723,"title":"Seasonal migrations of North Atlantic minke whales: novel insights from large-scale passive acoustic monitoring networks","created_at":"2021-10-14T09:11:34.959-07:00","owner_id":156002595,"url":"https://www.academia.edu/57846723/Seasonal_migrations_of_North_Atlantic_minke_whales_novel_insights_from_large_scale_passive_acoustic_monitoring_networks","slug":"Seasonal_migrations_of_North_Atlantic_minke_whales_novel_insights_from_large_scale_passive_acoustic_monitoring_networks","dom_id":"work_57846723","summary":"Background: Little is known about migration patterns and seasonal distribution away from coastal summer feeding habitats of many pelagic baleen whales. Recently, large-scale passive acoustic monitoring networks have become available to explore migration patterns and identify critical habitats of these species. North Atlantic minke whales (Balaenoptera acutorostrata) perform seasonal migrations between high latitude summer feeding and low latitude winter breeding grounds. While the distribution and abundance of the species has been studied across their summer range, data on migration and winter habitat are virtually missing. Acoustic recordings, from 16 different sites from across the North Atlantic, were analyzed to examine the seasonal and geographic variation in minke whale pulse train occurrence, infer information about migration routes and timing, and to identify possible winter habitats. Results: Acoustic detections show that minke whales leave their winter grounds south of 30°N from March through early April. On their southward migration in autumn, minke whales leave waters north of 40°N from mid-October through early November. In the western North Atlantic spring migrants appear to track the warmer waters of the Gulf Stream along the continental shelf, while whales travel farther offshore in autumn. Abundant detections were found off the southeastern US and the Caribbean during winter. Minke whale pulse trains showed evidence of geographic variation, with longer pulse trains recorded south of 40°N. Very few pulse trains were recorded during summer in any of the datasets. Conclusion: This study highlights the feasibility of using acoustic monitoring networks to explore migration patterns of pelagic marine mammals. Results confirm the presence of minke whales off the southeastern US and the Caribbean during winter months. The absence of pulse train detections during summer suggests either that minke whales switch their vocal behaviour at this time of year, are absent from available recording sites or that variation in signal structure influenced automated detection. Alternatively, if pulse trains are produced in a reproductive context by males, these data may indicate their absence from the selected recording sites. Evidence of geographic variation in pulse train duration suggests different behavioural functions or use of these calls at different latitudes.","publication":"Movement Ecology","publication_with_fallback":"Movement Ecology","downloadable_attachments":[{"id":72545736,"asset_id":57846723,"asset_type":"Work","always_allow_download":false,"scribd_thumbnail_url":"https://attachments.academia-assets.com/72545736/thumbnails/1.jpg","download_url":"https://d1wqtxts1xzle7.cloudfront.net/72545736/s40462-014-0024-3-libre.pdf?1634231025=\u0026response-content-disposition=attachment%3B+filename%3DSeasonal_migrations_of_North_Atlantic_mi.pdf\u0026Expires=1739921345\u0026Signature=eU7t37xeAyzkfTFvfWhzx3VFkDWIU8ZuN96FFuGBpjXEKNSN~B7tiCv3UucJE~RdZUSj03e1jnLiGvlaBonGmxSDp34tFfS4x3iONtwKQWmfhWxyYYaG9pF-HCWqNBVmJLUu6hHyuBdudGcxoYM4aR8wpJoT3D84vSP-77Bbl6dkAgBxJ9MP7BS2mIAn7eNFCmnVu9x7vH1P-6Qp8DT7fNz~rPlZ4WHUVtgupQT5w~vWtieVASTARdZoywz74BSp-Aggq25qWTUhgxJvmFr~-tFw4LW5-SM01dwiDUTGoEqoggyTLiqRexcpY7QcDYwn0zfZxZEJMCF07~dnTmPmyA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA","download_file_url":"https://www.academia.edu/attachments/72545736/download_file?st=MTczOTkxNzc0NSw4LjIyMi4yMDguMTQ2&","full_thumbnail_url":"https://0.academia-photos.com/attachment_thumbnails/72545736/mini_magick20211014-9461-139cj7w.png?1634232236"}],"downloadable_attachments_with_full_thumbnails":[{"id":72545736,"asset_id":57846723,"asset_type":"Work","always_allow_download":false,"scribd_thumbnail_url":"https://attachments.academia-assets.com/72545736/thumbnails/1.jpg","download_url":"https://d1wqtxts1xzle7.cloudfront.net/72545736/s40462-014-0024-3-libre.pdf?1634231025=\u0026response-content-disposition=attachment%3B+filename%3DSeasonal_migrations_of_North_Atlantic_mi.pdf\u0026Expires=1739921345\u0026Signature=eU7t37xeAyzkfTFvfWhzx3VFkDWIU8ZuN96FFuGBpjXEKNSN~B7tiCv3UucJE~RdZUSj03e1jnLiGvlaBonGmxSDp34tFfS4x3iONtwKQWmfhWxyYYaG9pF-HCWqNBVmJLUu6hHyuBdudGcxoYM4aR8wpJoT3D84vSP-77Bbl6dkAgBxJ9MP7BS2mIAn7eNFCmnVu9x7vH1P-6Qp8DT7fNz~rPlZ4WHUVtgupQT5w~vWtieVASTARdZoywz74BSp-Aggq25qWTUhgxJvmFr~-tFw4LW5-SM01dwiDUTGoEqoggyTLiqRexcpY7QcDYwn0zfZxZEJMCF07~dnTmPmyA__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA","download_file_url":"https://www.academia.edu/attachments/72545736/download_file?st=MTczOTkxNzc0NSw4LjIyMi4yMDguMTQ2&","full_thumbnail_url":"https://0.academia-photos.com/attachment_thumbnails/72545736/mini_magick20211014-9461-139cj7w.png?1634232236"}],"has_pdf":true,"has_fulltext":true,"page_count":17,"ordered_authors":[{"id":156002595,"first_name":"Peter","last_name":"Dugan","domain_name":"cornell","page_name":"PeterDugan","display_name":"Peter Dugan","profile_url":"https://cornell.academia.edu/PeterDugan","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":2982,"name":"Movement Ecology","url":"https://www.academia.edu/Documents/in/Movement_Ecology","nofollow":true}],"publication_year":2014,"publication_year_with_fallback":2014,"paper_rank":null,"all_time_views":10,"active_discussion":{}}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_57846725" data-work_id="57846725" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/57846725/Bioacoustic_Signal_Classification_Based_on_Continuous_Region_Processing_Grid_Masking_and_Artificial_Neural_Network">Bioacoustic Signal Classification Based on Continuous Region Processing, Grid Masking and Artificial Neural Network</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this paper, we develop a novel method based on machine-learning and image processing to identify North Atlantic right whale (NARW) upcalls in the presence of high levels of ambient and interfering noise. We apply a continuous region... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_57846725" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper, we develop a novel method based on machine-learning and image processing to identify North Atlantic right whale (NARW) upcalls in the presence of high levels of ambient and interfering noise. We apply a continuous region algorithm on the spectrogram to extract the regions of interest, and then use grid masking techniques to generate a small feature set that is then used in an artificial neural network classifier to identify the NARW up-calls. It is shown that the proposed technique is effective in detecting and capturing even very faint up-calls, in the presence of ambient and interfering noises. The method is evaluated on a dataset recorded in Massachusetts Bay, United States. The dataset includes 20000 sound clips for training, and 10000 sound clips for testing. The results show that the proposed technique can achieve an error rate of less than FPR = 4.5% for a 90% true positive rate.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/57846725" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9e5ebf4231e437e8e5b5db075c421674" rel="nofollow" data-download="{"attachment_id":72545731,"asset_id":57846725,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/72545731/download_file?st=MTczOTkxNzc0Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="156002595" href="https://cornell.academia.edu/PeterDugan">Peter Dugan</a><script data-card-contents-for-user="156002595" type="text/json">{"id":156002595,"first_name":"Peter","last_name":"Dugan","domain_name":"cornell","page_name":"PeterDugan","display_name":"Peter Dugan","profile_url":"https://cornell.academia.edu/PeterDugan","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_57846725 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="57846725"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 57846725, container: ".js-paper-rank-work_57846725", }); });</script></li><li class="js-percentile-work_57846725 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 57846725; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_57846725"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_57846725 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="57846725"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 57846725; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=57846725]").text(description); $(".js-view-count-work_57846725").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_57846725").removeClass('hidden') })</script></div></li></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_57846726" data-work_id="57846726" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/57846726/Bioacoustical_periodic_pulse_train_signal_detection_and_classification_using_spectrogram_intensity_binarization_and_energy_projection">Bioacoustical periodic pulse train signal detection and classification using spectrogram intensity binarization and energy projection</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">The following work outlines an approach for automatic detection and recognition of periodic pulse train signals using a multi-stage process based on spectrogram edge detection, energy projection and classification. The method has been... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_57846726" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">The following work outlines an approach for automatic detection and recognition of periodic pulse train signals using a multi-stage process based on spectrogram edge detection, energy projection and classification. The method has been implemented to automatically detect and recognize pulse train songs of minke whales. While the long term goal of this work is to properly identify and detect minke songs from large multi-year datasets, this effort was developed using sounds off the coast of Massachusetts, in the Stellwagen Bank National Marine Sanctuary. The detection methodology is presented and evaluated on 232 continuous hours of acoustic recordings and a qualitative analysis of machine learning classifiers and their performance is described. The trained automatic detection and classification system is applied to 120 continuous hours, comprised of various challenges such as broadband and narrowband noises, low SNR, and other pulse train signatures. This automatic system achieves a TPR of 63% for FPR of 0.6% (or 0.87 FP/h), at a Precision (PPV) of 84% and an F1 score of 71%.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/57846726" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="09167d67b4664dbcce44bc655fd31c5f" rel="nofollow" data-download="{"attachment_id":72545722,"asset_id":57846726,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/72545722/download_file?st=MTczOTkxNzc0Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="156002595" href="https://cornell.academia.edu/PeterDugan">Peter Dugan</a><script data-card-contents-for-user="156002595" type="text/json">{"id":156002595,"first_name":"Peter","last_name":"Dugan","domain_name":"cornell","page_name":"PeterDugan","display_name":"Peter Dugan","profile_url":"https://cornell.academia.edu/PeterDugan","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_57846726 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="57846726"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 57846726, container: ".js-paper-rank-work_57846726", }); });</script></li><li class="js-percentile-work_57846726 InlineList-item InlineList-item--bordered hidden u-tcGrayDark"><span class="percentile-widget hidden"><span class="u-mr2x percentile-widget" style="display: none">•</span><span class="u-mr2x work-percentile"></span></span><script>$(function () { var workId = 57846726; window.Academia.workPercentilesFetcher.queue(workId, function (percentileText) { var container = $(".js-percentile-work_57846726"); container.find('.work-percentile').text(percentileText.charAt(0).toUpperCase() + percentileText.slice(1)); container.find('.percentile-widget').show(); container.find('.percentile-widget').removeClass('hidden'); }); });</script></li><li class="js-view-count-work_57846726 InlineList-item InlineList-item--bordered hidden"><div><span><span class="js-view-count view-count u-mr2x" data-work-id="57846726"><i class="fa fa-spinner fa-spin"></i></span><script>$(function () { var workId = 57846726; window.Academia.workViewCountsFetcher.queue(workId, function (count) { var description = window.$h.commaizeInt(count) + " " + window.$h.pluralize(count, 'View'); $(".js-view-count[data-work-id=57846726]").text(description); $(".js-view-count-work_57846726").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_57846726").removeClass('hidden') })</script></div></li></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_57846728" data-work_id="57846728" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/57846728/Classification_for_Big_Dataset_of_Bioacoustic_Signals_Based_on_Human_Scoring_System_and_Artificial_Neural_Network">Classification for Big Dataset of Bioacoustic Signals Based on Human Scoring System and Artificial Neural Network</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">In this paper, we propose a method to improve sound classification performance by combining signal features, derived from the time-frequency spectrogram, with human perception. The method presented herein exploits an artificial neural... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_57846728" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">In this paper, we propose a method to improve sound classification performance by combining signal features, derived from the time-frequency spectrogram, with human perception. The method presented herein exploits an artificial neural network (ANN) and learns the signal features based on the human perception knowledge. The proposed method is applied to a large acoustic dataset containing 24 months of nearly continuous recordings. The results show a significant improvement in performance of the detection-classification system; yielding as much as 20% improvement in true positive rate for a given false positive rate.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/57846728" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="9cf3517f2697a7cc23b6fe22cfcea1d0" rel="nofollow" data-download="{"attachment_id":72545728,"asset_id":57846728,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/72545728/download_file?st=MTczOTkxNzc0Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="156002595" href="https://cornell.academia.edu/PeterDugan">Peter Dugan</a><script data-card-contents-for-user="156002595" type="text/json">{"id":156002595,"first_name":"Peter","last_name":"Dugan","domain_name":"cornell","page_name":"PeterDugan","display_name":"Peter Dugan","profile_url":"https://cornell.academia.edu/PeterDugan","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_57846728 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="57846728"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 57846728, container: ".js-paper-rank-work_57846728", }); 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$(".js-view-count[data-work-id=57846728]").text(description); $(".js-view-count-work_57846728").attr('title', description).tooltip(); }); });</script></span><script>$(function() { $(".js-view-count-work_57846728").removeClass('hidden') })</script></div></li><li class="InlineList-item u-positionRelative" style="max-width: 250px"><div class="u-positionAbsolute" data-has-card-for-ri-list="57846728"><i class="fa fa-tag InlineList-item-icon u-positionRelative"></i>  <a class="InlineList-item-text u-positionRelative">7</a>  </div><span class="InlineList-item-text u-textTruncate u-pl9x"><a class="InlineList-item-text" data-has-card-for-ri="42620" rel="nofollow" href="https://www.academia.edu/Documents/in/Time-Frequency_Analysis">Time-Frequency Analysis</a>, <script data-card-contents-for-ri="42620" type="text/json">{"id":42620,"name":"Time-Frequency Analysis","url":"https://www.academia.edu/Documents/in/Time-Frequency_Analysis","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="205509" rel="nofollow" href="https://www.academia.edu/Documents/in/Time_Frequency_Analysis">Time Frequency Analysis</a>, <script data-card-contents-for-ri="205509" type="text/json">{"id":205509,"name":"Time Frequency Analysis","url":"https://www.academia.edu/Documents/in/Time_Frequency_Analysis","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="810880" rel="nofollow" href="https://www.academia.edu/Documents/in/Human_Perception">Human Perception</a>, <script data-card-contents-for-ri="810880" type="text/json">{"id":810880,"name":"Human Perception","url":"https://www.academia.edu/Documents/in/Human_Perception","nofollow":true}</script><a class="InlineList-item-text" data-has-card-for-ri="1032327" rel="nofollow" href="https://www.academia.edu/Documents/in/False_Positive_Rate">False Positive Rate</a><script data-card-contents-for-ri="1032327" type="text/json">{"id":1032327,"name":"False Positive Rate","url":"https://www.academia.edu/Documents/in/False_Positive_Rate","nofollow":true}</script></span></li><script>(function(){ if (true) { new Aedu.ResearchInterestListCard({ el: $('*[data-has-card-for-ri-list=57846728]'), work: {"id":57846728,"title":"Classification for Big Dataset of Bioacoustic Signals Based on Human Scoring System and Artificial Neural Network","created_at":"2021-10-14T09:11:35.251-07:00","owner_id":156002595,"url":"https://www.academia.edu/57846728/Classification_for_Big_Dataset_of_Bioacoustic_Signals_Based_on_Human_Scoring_System_and_Artificial_Neural_Network","slug":"Classification_for_Big_Dataset_of_Bioacoustic_Signals_Based_on_Human_Scoring_System_and_Artificial_Neural_Network","dom_id":"work_57846728","summary":"In this paper, we propose a method to improve sound classification performance by combining signal features, derived from the time-frequency spectrogram, with human perception. The method presented herein exploits an artificial neural network (ANN) and learns the signal features based on the human perception knowledge. The proposed method is applied to a large acoustic dataset containing 24 months of nearly continuous recordings. The results show a significant improvement in performance of the detection-classification system; yielding as much as 20% improvement in true positive rate for a given false positive rate.","publication":null,"publication_with_fallback":null,"downloadable_attachments":[{"id":72545728,"asset_id":57846728,"asset_type":"Work","always_allow_download":false,"scribd_thumbnail_url":"https://attachments.academia-assets.com/72545728/thumbnails/1.jpg","download_url":"https://d1wqtxts1xzle7.cloudfront.net/72545728/1305.3633-libre.pdf?1634231021=\u0026response-content-disposition=attachment%3B+filename%3DClassification_for_Big_Dataset_of_Bioaco.pdf\u0026Expires=1739921346\u0026Signature=JhAoQWERA~0lFuTn9R134HEc97tlmv2G7aDHBZwRsRC76e1INmK8mOfiT32Kj0clGmnuVWr1DlBm4uFxAg4nEdKg32RsLueeIByHSRN8cu-UuO2DlplfZunCwOjWKZ9uODx1ZJIzHIjAhAa0o3wMvN-~Q4JyUbtbv5D~P2dhTWGphdsLAR00Kasco~Uuoo7LpyW6aVPFwUz1TekgZM6KnV82DBdSBkwv12nfhSUS2GNb1mK7lxy1NvrBT~3NpWGYsBQ2O31oEG9DwuZrbdAtFevfgiwfjIglcpVqAJenuYKSW82qNuYA2Jpj9hztX1p~8l0TeOdNxLKfxtDbSPDp5w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA","download_file_url":"https://www.academia.edu/attachments/72545728/download_file?st=MTczOTkxNzc0Niw4LjIyMi4yMDguMTQ2&","full_thumbnail_url":"https://0.academia-photos.com/attachment_thumbnails/72545728/mini_magick20211014-4269-16orj1i.png?1634232255"}],"downloadable_attachments_with_full_thumbnails":[{"id":72545728,"asset_id":57846728,"asset_type":"Work","always_allow_download":false,"scribd_thumbnail_url":"https://attachments.academia-assets.com/72545728/thumbnails/1.jpg","download_url":"https://d1wqtxts1xzle7.cloudfront.net/72545728/1305.3633-libre.pdf?1634231021=\u0026response-content-disposition=attachment%3B+filename%3DClassification_for_Big_Dataset_of_Bioaco.pdf\u0026Expires=1739921346\u0026Signature=JhAoQWERA~0lFuTn9R134HEc97tlmv2G7aDHBZwRsRC76e1INmK8mOfiT32Kj0clGmnuVWr1DlBm4uFxAg4nEdKg32RsLueeIByHSRN8cu-UuO2DlplfZunCwOjWKZ9uODx1ZJIzHIjAhAa0o3wMvN-~Q4JyUbtbv5D~P2dhTWGphdsLAR00Kasco~Uuoo7LpyW6aVPFwUz1TekgZM6KnV82DBdSBkwv12nfhSUS2GNb1mK7lxy1NvrBT~3NpWGYsBQ2O31oEG9DwuZrbdAtFevfgiwfjIglcpVqAJenuYKSW82qNuYA2Jpj9hztX1p~8l0TeOdNxLKfxtDbSPDp5w__\u0026Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA","download_file_url":"https://www.academia.edu/attachments/72545728/download_file?st=MTczOTkxNzc0Niw4LjIyMi4yMDguMTQ2&","full_thumbnail_url":"https://0.academia-photos.com/attachment_thumbnails/72545728/mini_magick20211014-4269-16orj1i.png?1634232255"}],"has_pdf":true,"has_fulltext":true,"page_count":6,"ordered_authors":[{"id":156002595,"first_name":"Peter","last_name":"Dugan","domain_name":"cornell","page_name":"PeterDugan","display_name":"Peter Dugan","profile_url":"https://cornell.academia.edu/PeterDugan","photo":"/images/s65_no_pic.png"}],"research_interests":[{"id":42620,"name":"Time-Frequency Analysis","url":"https://www.academia.edu/Documents/in/Time-Frequency_Analysis","nofollow":true},{"id":205509,"name":"Time Frequency Analysis","url":"https://www.academia.edu/Documents/in/Time_Frequency_Analysis","nofollow":true},{"id":810880,"name":"Human Perception","url":"https://www.academia.edu/Documents/in/Human_Perception","nofollow":true},{"id":1032327,"name":"False Positive Rate","url":"https://www.academia.edu/Documents/in/False_Positive_Rate","nofollow":true},{"id":1211304,"name":"Artificial Neural Network","url":"https://www.academia.edu/Documents/in/Artificial_Neural_Network"},{"id":3340400,"name":"Scoring system","url":"https://www.academia.edu/Documents/in/Scoring_system-1"},{"id":4017214,"name":"True Positive","url":"https://www.academia.edu/Documents/in/True_Positive"}],"publication_year":null,"publication_year_with_fallback":null,"paper_rank":null,"all_time_views":6,"active_discussion":{}}, }) } })();</script></ul></li></ul></div></div><div class="u-borderBottom1 u-borderColorGrayLighter"><div class="clearfix u-pv7x u-mb0x js-work-card work_57846731" data-work_id="57846731" itemscope="itemscope" itemtype="https://schema.org/ScholarlyArticle"><div class="header"><div class="title u-fontSerif u-fs22 u-lineHeight1_3"><a class="u-tcGrayDarkest js-work-link" href="https://www.academia.edu/57846731/North_Atlantic_Right_Whale_acoustic_signal_processing_Part_I_comparison_of_machine_learning_recognition_algorithms">North Atlantic Right Whale acoustic signal processing: Part I. comparison of machine learning recognition algorithms</a></div></div><div class="u-pb4x u-mt3x"><div class="summary u-fs14 u-fw300 u-lineHeight1_5 u-tcGrayDarkest"><div class="summarized">This paper compares three different approaches currently used in recognizing contact calls made from the North Atlantic Right Whale (NRW), Eubalaena glacialis. We present two new approaches consisting of machine learning algorithms based... <a class="more_link u-tcGrayDark u-linkUnstyled" data-container=".work_57846731" data-show=".complete" data-hide=".summarized" data-more-link-behavior="true" href="#">more</a></div><div class="complete hidden">This paper compares three different approaches currently used in recognizing contact calls made from the North Atlantic Right Whale (NRW), Eubalaena glacialis. We present two new approaches consisting of machine learning algorithms based on artificial neural networks (NET) and the classification and regression tree classifiers (CART), and compare their performance with earlier work that employs multi-Stage feature vector testing (FVT) approach. A combined total of over 100,000 noise and NRW up-call events were used in the study. Calls were primarily recorded from two areas, Cape Cod Bay and Great South Channel. Of the three classifiers, the CART had the highest assignment rates, overall 86.45% with highest false positive rates (<100 per hour). The FVT Method had exceptionally low false positive rates, with <50 per hour. However, it had an overall assignment rate less than the NET. The CART had statistically the same false positive rate as the NET with the highest assignment rates, 2.2% higher than the NET and 11.75% greater than the FVT Method. Details of the results are shown and extensions to the research are discussed.</div></div></div><ul class="InlineList u-ph0x u-fs13"><li class="InlineList-item logged_in_only"><div class="share_on_academia_work_button"><a class="academia_share Button Button--inverseBlue Button--sm js-bookmark-button" data-academia-share="Work/57846731" data-share-source="work_strip" data-spinner="small_white_hide_contents"><i class="fa fa-plus"></i><span class="work-strip-link-text u-ml1x" data-content="button_text">Bookmark</span></a></div></li><li class="InlineList-item"><div class="download"><a id="b9492f1217b6db3e56a324d1f8422e4d" rel="nofollow" data-download="{"attachment_id":72547974,"asset_id":57846731,"asset_type":"Work","always_allow_download":false,"track":null,"button_location":"work_strip","source":null,"hide_modal":null}" class="Button Button--sm Button--inverseGreen js-download-button prompt_button doc_download" href="https://www.academia.edu/attachments/72547974/download_file?st=MTczOTkxNzc0Niw4LjIyMi4yMDguMTQ2&s=work_strip"><i class="fa fa-arrow-circle-o-down fa-lg"></i><span class="u-textUppercase u-ml1x" data-content="button_text">Download</span></a></div></li><li class="InlineList-item"><ul class="InlineList InlineList--bordered u-ph0x"><li class="InlineList-item InlineList-item--bordered"><span class="InlineList-item-text">by <span itemscope="itemscope" itemprop="author" itemtype="https://schema.org/Person"><a class="u-tcGrayDark u-fw700" data-has-card-for-user="156002595" href="https://cornell.academia.edu/PeterDugan">Peter Dugan</a><script data-card-contents-for-user="156002595" type="text/json">{"id":156002595,"first_name":"Peter","last_name":"Dugan","domain_name":"cornell","page_name":"PeterDugan","display_name":"Peter Dugan","profile_url":"https://cornell.academia.edu/PeterDugan","photo":"/images/s65_no_pic.png"}</script></span></span></li><li class="js-paper-rank-work_57846731 InlineList-item InlineList-item--bordered hidden"><span class="js-paper-rank-view hidden u-tcGrayDark" data-paper-rank-work-id="57846731"><i class="u-m1x fa fa-bar-chart"></i><strong class="js-paper-rank"></strong></span><script>$(function() { new Works.PaperRankView({ workId: 57846731, container: ".js-paper-rank-work_57846731", }); 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We present two new approaches consisting of machine learning algorithms based on artificial neural networks (NET) and the classification and regression tree classifiers (CART), and compare their performance with earlier work that employs multi-Stage feature vector testing (FVT) approach. A combined total of over 100,000 noise and NRW up-call events were used in the study. Calls were primarily recorded from two areas, Cape Cod Bay and Great South Channel. Of the three classifiers, the CART had the highest assignment rates, overall 86.45% with highest false positive rates (\u003c100 per hour). The FVT Method had exceptionally low false positive rates, with \u003c50 per hour. However, it had an overall assignment rate less than the NET. The CART had statistically the same false positive rate as the NET with the highest assignment rates, 2.2% higher than the NET and 11.75% greater than the FVT Method. 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