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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.
B. Schuller, A. Batliner, S. Steidl, and D. Seppi, “Recognising Realistic Emotions and Affect in Speech: State of the Art and Lessons Learnt from the First Challenge,”
2011
Earlier work this paper cites.
A. Rosenberg, “Classifying skewed data: Importance weighting to optimize average recall,” in
2012
Earlier work this paper cites.
I. Lefter, L. J. Rothkrantz, and G. J. Burghouts, “A comparative study on automatic audio–visual fusion for aggression detection using meta-information,”
2013
Earlier work this paper cites.
B. Schuller, S. Steidl, A. Batliner, A. Vinciarelli, K. Scherer, F. Ringeval, M. Chetouani, F. Weninger, F. Eyben, E. Marchi, M. Mortillaro, H. Salamin, A. Polychroniou, F. Valente, and S. Kim, “The Interspeech 2013 Computational Paralinguistics Challenge: Social Signals, Conflict, Emotion, Autism,” in
2013
Earlier work this paper cites.
F. Eyben, F. Weninger, F. Groß, and B. Schuller, “Recent Developments in openSMILE, the Munich Open-Source Multimedia Feature Extractor,” in
2013
Earlier work this paper cites.
F. Weninger, F. Eyben, B. Schuller, M. Mortillaro, and K. R. Scherer, “On the Acoustics of Emotion in Audio: What Speech, Music and Sound have in Common,”
2013
Earlier work this paper cites.
B. Schuller and A. Batliner,
2014
Earlier work this paper cites.
I. Lefter, G. J. Burghouts, and L. J. Rothkrantz, “An audio-visual dataset of human–human interactions in stressful situations,”
2014
Earlier work this paper cites.
S. Heinicke, A. K. Kalan, O. J. Wagner, R. Mundry, H. Lukashevich, and H. S. Kühl, “Assessing the performance of a semi-automated acoustic monitoring system for primates,”
2015
Earlier work this paper cites.
H. Lim, M. J. Kim, and H. Kim, “Robust Sound Event Classification Using LBP-HOG Based Bag-of-Audio-Words Feature Representation,” in
2015
Earlier work this paper cites.
P. Fedurek, K. Zuberbühler, and C. D. Dahl, “Sequential information in a great ape utterance,”
2016
Earlier work this paper cites.
M. Schmitt, F. Ringeval, and B. Schuller, “At the Border of Acoustics and Linguistics: Bag-of-Audio-Words for the Recognition of Emotions in Speech,” in
2016
Earlier work this paper cites.
P. H. Wrege, E. D. Rowland, S. Keen, and Y. Shiu, “Acoustic monitoring for conservation in tropical forests: examples from forest elephants,”
2017
Cited alongside, same era.
M. Schmitt and B. W. Schuller, “openXBOW – Introducing the Passau Open-Source Crossmodal Bag-of-Words Toolkit,”
2017
Cited alongside, same era.
S. Amiriparian, M. Gerczuk, S. Ottl, N. Cummins, M. Freitag, S. Pugachevskiy, and B. Schuller, “Snore sound classification using image-based deep spectrum features,” in
2017
Cited alongside, same era.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in
2017
Cited alongside, same era.
S. Amiriparian, M. Freitag, N. Cummins, and B. Schuller, “Sequence to Sequence Autoencoders for Unsupervised Representation Learning from Audio,” in
2017
Cited alongside, same era.
A. P. Hill, P. Prince, J. L. Snaddon, C. P. Doncaster, and A. Rogers, “Audiomoth: A low-cost acoustic device for monitoring biodiversity and the environment,”
2019
Later among the works it cites.
2019
Later among the works it cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in
2019
Later among the works it cites.
2020
Later among the works it cites.
C. Brown, J. Chauhan, A. Grammenos, J. Han, A. Hasthanasombat, D. Spathis, T. Xia, P. Cicuta, and C. Mascolo, “Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data,” in
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N. Priyadarshani, S. Marsland, and I. Castro, “Automated birdsong recognition in complex acoustic environments: a review,”
2018
Cited alongside, same era.
S. Amiriparian, M. Gerczuk, S. Ottl, N. Cummins, S. Pugachevskiy, and B. Schuller, “Bag-of-deep-features: Noise-robust deep feature representations for audio analysis,” in
2018
Cited alongside, same era.
M. Freitag, S. Amiriparian, S. Pugachevskiy, N. Cummins, and B. Schuller, “auDeep: Unsupervised Learning of Representations from Audio with Deep Recurrent Neural Networks,”
2018
Cited alongside, same era.
2018
Cited alongside, same era.
P. Tzirakis, J. Zhang, and B. W. Schuller, “End-to-end speech emotion recognition using deep neural networks,” in
2018
Cited alongside, same era.
D. J. Clink, M. C. Crofoot, and A. J. Marshall, “Application of a semi-automated vocal fingerprinting approach to monitor Bornean gibbon females in an experimentally fragmented landscape in Sabah, Malaysia,”
2019
Cited alongside, same era.
2020
Later among the works it cites.
L. Stappen, B. Schuller, I. Lefter, E. Cambria, and I. Kompatsiaris, “Summary of muse 2020: Multimodal sentiment analysis, emotion-target engagement and trustworthiness detection in real-life media,” in
2020
Later among the works it cites.
L. Stappen, G. Rizos, M. Hasan, T. Hain, and B. W. Schuller, “Uncertainty-aware machine support for paper reviewing on the interspeech 2019 submission corpus,”
2020
Later among the works it cites.
B. W. Schuller, A. Batliner, C. Bergler, E.-M. Messner, A. Hamilton, S. Amiriparian, A. Baird, G. Rizos, M. Schmitt, L. Stappen
2020
Later among the works it cites.
J. Han, C. Brown, J. Chauhan, A. Grammenos, A. Hasthanasombat, D. Spathis, T. Xia, P. Cicuta, and C. Mascolo, “Exploring Automatic COVID-19 Diagnosis via Voice and Symptoms from Crowdsourced Data,” in
2021
Closest in time.
2021
Closest in time.
L. Stappen, A. Baird, E. Cambria, and B. W. Schuller, “Sentiment analysis and topic recognition in video transcriptions,”
2021
Closest in time.