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</div> <div class="control"> <button class="button is-small 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/2404.00470">arXiv:2404.00470</a> <span> [<a href="https://arxiv.org/pdf/2404.00470">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</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="Audio and Speech Processing">eess.AS</span> </div> </div> <p class="title is-5 mathjax"> Classification of Short Segment Pediatric Heart Sounds Based on a Transformer-Based Convolutional Neural Network </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hassanuzzaman%2C+M">Md Hassanuzzaman</a>, <a href="/search/cs?searchtype=author&query=Hasan%2C+N+A">Nurul Akhtar Hasan</a>, <a href="/search/cs?searchtype=author&query=Mamun%2C+M+A+A">Mohammad Abdullah Al Mamun</a>, <a href="/search/cs?searchtype=author&query=Ahmed%2C+K+I">Khawza I Ahmed</a>, <a href="/search/cs?searchtype=author&query=Khandoker%2C+A+H">Ahsan H Khandoker</a>, <a href="/search/cs?searchtype=author&query=Mostafa%2C+R">Raqibul Mostafa</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="2404.00470v1-abstract-short" style="display: inline;"> Congenital anomalies arising as a result of a defect in the structure of the heart and great vessels are known as congenital heart diseases or CHDs. A PCG can provide essential details about the mechanical conduction system of the heart and point out specific patterns linked to different kinds of CHD. This study aims to investigate the minimum signal duration required for the automatic classificat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00470v1-abstract-full').style.display = 'inline'; document.getElementById('2404.00470v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.00470v1-abstract-full" style="display: none;"> Congenital anomalies arising as a result of a defect in the structure of the heart and great vessels are known as congenital heart diseases or CHDs. A PCG can provide essential details about the mechanical conduction system of the heart and point out specific patterns linked to different kinds of CHD. This study aims to investigate the minimum signal duration required for the automatic classification of heart sounds. This study also investigated the optimum signal quality assessment indicator (Root Mean Square of Successive Differences) RMSSD and (Zero Crossings Rate) ZCR value. Mel-frequency cepstral coefficients (MFCCs) based feature is used as an input to build a Transformer-Based residual one-dimensional convolutional neural network, which is then used for classifying the heart sound. The study showed that 0.4 is the ideal threshold for getting suitable signals for the RMSSD and ZCR indicators. Moreover, a minimum signal length of 5s is required for effective heart sound classification. It also shows that a shorter signal (3 s heart sound) does not have enough information to categorize heart sounds accurately, and the longer signal (15 s heart sound) may contain more noise. The best accuracy, 93.69%, is obtained for the 5s signal to distinguish the heart sound. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00470v1-abstract-full').style.display = 'none'; document.getElementById('2404.00470v1-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> 30 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </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">16 pages,11 Figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.06923">arXiv:2402.06923</a> <span> [<a href="https://arxiv.org/pdf/2402.06923">pdf</a>, <a href="https://arxiv.org/format/2402.06923">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="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Sound">cs.SD</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Signal Processing">eess.SP</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">stat.ML</span> </div> </div> <p class="title is-5 mathjax"> CochCeps-Augment: A Novel Self-Supervised Contrastive Learning Using Cochlear Cepstrum-based Masking for Speech Emotion Recognition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ziogas%2C+I">Ioannis Ziogas</a>, <a href="/search/cs?searchtype=author&query=Alfalahi%2C+H">Hessa Alfalahi</a>, <a href="/search/cs?searchtype=author&query=Khandoker%2C+A+H">Ahsan H. Khandoker</a>, <a href="/search/cs?searchtype=author&query=Hadjileontiadis%2C+L+J">Leontios J. Hadjileontiadis</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="2402.06923v1-abstract-short" style="display: inline;"> Self-supervised learning (SSL) for automated speech recognition in terms of its emotional content, can be heavily degraded by the presence noise, affecting the efficiency of modeling the intricate temporal and spectral informative structures of speech. Recently, SSL on large speech datasets, as well as new audio-specific SSL proxy tasks, such as, temporal and frequency masking, have emerged, yield… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.06923v1-abstract-full').style.display = 'inline'; document.getElementById('2402.06923v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.06923v1-abstract-full" style="display: none;"> Self-supervised learning (SSL) for automated speech recognition in terms of its emotional content, can be heavily degraded by the presence noise, affecting the efficiency of modeling the intricate temporal and spectral informative structures of speech. Recently, SSL on large speech datasets, as well as new audio-specific SSL proxy tasks, such as, temporal and frequency masking, have emerged, yielding superior performance compared to classic approaches drawn from the image augmentation domain. Our proposed contribution builds upon this successful paradigm by introducing CochCeps-Augment, a novel bio-inspired masking augmentation task for self-supervised contrastive learning of speech representations. Specifically, we utilize the newly introduced bio-inspired cochlear cepstrogram (CCGRAM) to derive noise robust representations of input speech, that are then further refined through a self-supervised learning scheme. The latter employs SimCLR to generate contrastive views of a CCGRAM through masking of its angle and quefrency dimensions. Our experimental approach and validations on the emotion recognition K-EmoCon benchmark dataset, for the first time via a speaker-independent approach, features unsupervised pre-training, linear probing and fine-tuning. Our results potentiate CochCeps-Augment to serve as a standard tool in speech emotion recognition analysis, showing the added value of incorporating bio-inspired masking as an informative augmentation task for self-supervision. Our code for implementing CochCeps-Augment will be made available at: https://github.com/GiannisZgs/CochCepsAugment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.06923v1-abstract-full').style.display = 'none'; document.getElementById('2402.06923v1-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> 10 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </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, 1 figure Accepted in IEEE ICASSP 2024 Workshops - Self-Supervision in Audio, Speech, and Beyond</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2005.04120">arXiv:2005.04120</a> <span> [<a href="https://arxiv.org/pdf/2005.04120">pdf</a>, <a href="https://arxiv.org/format/2005.04120">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.1038/s41597-020-00630-y">10.1038/s41597-020-00630-y <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> K-EmoCon, a multimodal sensor dataset for continuous emotion recognition in naturalistic conversations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Park%2C+C+Y">Cheul Young Park</a>, <a href="/search/cs?searchtype=author&query=Cha%2C+N">Narae Cha</a>, <a href="/search/cs?searchtype=author&query=Kang%2C+S">Soowon Kang</a>, <a href="/search/cs?searchtype=author&query=Kim%2C+A">Auk Kim</a>, <a href="/search/cs?searchtype=author&query=Khandoker%2C+A+H">Ahsan Habib Khandoker</a>, <a href="/search/cs?searchtype=author&query=Hadjileontiadis%2C+L">Leontios Hadjileontiadis</a>, <a href="/search/cs?searchtype=author&query=Oh%2C+A">Alice Oh</a>, <a href="/search/cs?searchtype=author&query=Jeong%2C+Y">Yong Jeong</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+U">Uichin Lee</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="2005.04120v2-abstract-short" style="display: inline;"> Recognizing emotions during social interactions has many potential applications with the popularization of low-cost mobile sensors, but a challenge remains with the lack of naturalistic affective interaction data. Most existing emotion datasets do not support studying idiosyncratic emotions arising in the wild as they were collected in constrained environments. Therefore, studying emotions in the… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.04120v2-abstract-full').style.display = 'inline'; document.getElementById('2005.04120v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2005.04120v2-abstract-full" style="display: none;"> Recognizing emotions during social interactions has many potential applications with the popularization of low-cost mobile sensors, but a challenge remains with the lack of naturalistic affective interaction data. Most existing emotion datasets do not support studying idiosyncratic emotions arising in the wild as they were collected in constrained environments. Therefore, studying emotions in the context of social interactions requires a novel dataset, and K-EmoCon is such a multimodal dataset with comprehensive annotations of continuous emotions during naturalistic conversations. The dataset contains multimodal measurements, including audiovisual recordings, EEG, and peripheral physiological signals, acquired with off-the-shelf devices from 16 sessions of approximately 10-minute long paired debates on a social issue. Distinct from previous datasets, it includes emotion annotations from all three available perspectives: self, debate partner, and external observers. Raters annotated emotional displays at intervals of every 5 seconds while viewing the debate footage, in terms of arousal-valence and 18 additional categorical emotions. The resulting K-EmoCon is the first publicly available emotion dataset accommodating the multiperspective assessment of emotions during social interactions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2005.04120v2-abstract-full').style.display = 'none'; document.getElementById('2005.04120v2-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> 19 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 8 May, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2020. </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">20 pages, 4 figures, for associated dataset, see https://doi.org/10.5281/zenodo.3814370</span> </p> <p class="comments is-size-7"> <span 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