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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2102.13468">arXiv:2102.13468</a> <span> [<a href="https://arxiv.org/pdf/2102.13468">pdf</a>, <a href="https://arxiv.org/format/2102.13468">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Audio and Speech Processing">eess.AS</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> </div> </div> <p class="title is-5 mathjax"> The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Schuller%2C+B+W">Bj枚rn W. Schuller</a>, <a href="/search/cs?searchtype=author&query=Batliner%2C+A">Anton Batliner</a>, <a href="/search/cs?searchtype=author&query=Bergler%2C+C">Christian Bergler</a>, <a href="/search/cs?searchtype=author&query=Mascolo%2C+C">Cecilia Mascolo</a>, <a href="/search/cs?searchtype=author&query=Han%2C+J">Jing Han</a>, <a href="/search/cs?searchtype=author&query=Lefter%2C+I">Iulia Lefter</a>, <a href="/search/cs?searchtype=author&query=Kaya%2C+H">Heysem Kaya</a>, <a href="/search/cs?searchtype=author&query=Amiriparian%2C+S">Shahin Amiriparian</a>, <a href="/search/cs?searchtype=author&query=Baird%2C+A">Alice Baird</a>, <a href="/search/cs?searchtype=author&query=Stappen%2C+L">Lukas Stappen</a>, <a href="/search/cs?searchtype=author&query=Ottl%2C+S">Sandra Ottl</a>, <a href="/search/cs?searchtype=author&query=Gerczuk%2C+M">Maurice Gerczuk</a>, <a href="/search/cs?searchtype=author&query=Tzirakis%2C+P">Panagiotis Tzirakis</a>, <a href="/search/cs?searchtype=author&query=Brown%2C+C">Chlo毛 Brown</a>, <a href="/search/cs?searchtype=author&query=Chauhan%2C+J">Jagmohan Chauhan</a>, <a href="/search/cs?searchtype=author&query=Grammenos%2C+A">Andreas Grammenos</a>, <a href="/search/cs?searchtype=author&query=Hasthanasombat%2C+A">Apinan Hasthanasombat</a>, <a href="/search/cs?searchtype=author&query=Spathis%2C+D">Dimitris Spathis</a>, <a href="/search/cs?searchtype=author&query=Xia%2C+T">Tong Xia</a>, <a href="/search/cs?searchtype=author&query=Cicuta%2C+P">Pietro Cicuta</a>, <a href="/search/cs?searchtype=author&query=Rothkrantz%2C+L+J+M">Leon J. M. Rothkrantz</a>, <a href="/search/cs?searchtype=author&query=Zwerts%2C+J">Joeri Zwerts</a>, <a href="/search/cs?searchtype=author&query=Treep%2C+J">Jelle Treep</a>, <a href="/search/cs?searchtype=author&query=Kaandorp%2C+C">Casper Kaandorp</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2102.13468v1-abstract-short" style="display: inline;"> The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of es… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.13468v1-abstract-full').style.display = 'inline'; document.getElementById('2102.13468v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2102.13468v1-abstract-full" style="display: none;"> The INTERSPEECH 2021 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the COVID-19 Cough and COVID-19 Speech Sub-Challenges, a binary classification on COVID-19 infection has to be made based on coughing sounds and speech; in the Escalation SubChallenge, a three-way assessment of the level of escalation in a dialogue is featured; and in the Primates Sub-Challenge, four species vs background need to be classified. We describe the Sub-Challenges, baseline feature extraction, and classifiers based on the 'usual' COMPARE and BoAW features as well as deep unsupervised representation learning using the AuDeep toolkit, and deep feature extraction from pre-trained CNNs using the Deep Spectrum toolkit; in addition, we add deep end-to-end sequential modelling, and partially linguistic analysis. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2102.13468v1-abstract-full').style.display = 'none'; document.getElementById('2102.13468v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 24 February, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68 <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7; I.5.0; J.3 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2101.10390">arXiv:2101.10390</a> <span> [<a href="https://arxiv.org/pdf/2101.10390">pdf</a>, <a href="https://arxiv.org/format/2101.10390">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Populations and Evolution">q-bio.PE</span> </div> </div> <p class="title is-5 mathjax"> Introducing a Central African Primate Vocalisation Dataset for Automated Species Classification </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Zwerts%2C+J+A">Joeri A. Zwerts</a>, <a href="/search/cs?searchtype=author&query=Treep%2C+J">Jelle Treep</a>, <a href="/search/cs?searchtype=author&query=Kaandorp%2C+C+S">Casper S. Kaandorp</a>, <a href="/search/cs?searchtype=author&query=Meewis%2C+F">Floor Meewis</a>, <a href="/search/cs?searchtype=author&query=Koot%2C+A+C">Amparo C. Koot</a>, <a href="/search/cs?searchtype=author&query=Kaya%2C+H">Heysem Kaya</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2101.10390v1-abstract-short" style="display: inline;"> Automated classification of animal vocalisations is a potentially powerful wildlife monitoring tool. Training robust classifiers requires sizable annotated datasets, which are not easily recorded in the wild. To circumvent this problem, we recorded four primate species under semi-natural conditions in a wildlife sanctuary in Cameroon with the objective to train a classifier capable of detecting sp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.10390v1-abstract-full').style.display = 'inline'; document.getElementById('2101.10390v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2101.10390v1-abstract-full" style="display: none;"> Automated classification of animal vocalisations is a potentially powerful wildlife monitoring tool. Training robust classifiers requires sizable annotated datasets, which are not easily recorded in the wild. To circumvent this problem, we recorded four primate species under semi-natural conditions in a wildlife sanctuary in Cameroon with the objective to train a classifier capable of detecting species in the wild. Here, we introduce the collected dataset, describe our approach and initial results of classifier development. To increase the efficiency of the annotation process, we condensed the recordings with an energy/change based automatic vocalisation detection. Segmenting the annotated chunks into training, validation and test sets, initial results reveal up to 82% unweighted average recall (UAR) test set performance in four-class primate species classification. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2101.10390v1-abstract-full').style.display = 'none'; document.getElementById('2101.10390v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 January, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2021. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">5 pages, 3 figures, 2 tables</span> </p> </li> </ol> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a 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