| The interspeech 2016 computational paralinguistics challenge: Deception, sincerity and native language B Schuller, S Steidl, A Batliner, J Hirschberg, JK Burgoon, A Baird, ... | 387 | 2016 |
| Snore sound classification using image-based deep spectrum features S Amiriparian, M Gerczuk, S Ottl, N Cummins, M Freitag, S Pugachevskiy, ... Proceedings INTERSPEECH, 3512-3516, 2017 | 309 | 2017 |
| Speech analysis for health: current state-of-the-art and the increasing impact of deep learning N Cummins, A Baird, BW Schuller Methods 151, 41-54, 2018 | 182 | 2018 |
| The interspeech 2020 computational paralinguistics challenge: Elderly emotion, breathing & masks BW Schuller, A Batliner, C Bergler, EM Messner, A Hamilton, ... | 145 | 2020 |
| The interspeech 2018 computational paralinguistics challenge: Atypical & self-assessed affect, crying & heart beats BW Schuller, S Steidl, A Batliner, PB Marschik, H Baumeister, F Dong, ... Proceedings of INTERSPEECH, Hyderabad, India 5, 2018 | 145 | 2018 |
| Deep scalogram representations for acoustic scene classification Z Ren, K Qian, Z Zhang, V Pandit, A Baird, B Schuller IEEE/CAA Journal of Automatica Sinica 5 (3), 662-669, 2018 | 141 | 2018 |
| The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 cough, COVID-19 speech, escalation & primates BW Schuller, A Batliner, C Bergler, C Mascolo, J Han, I Lefter, H Kaya, ... Proceedings of Interspeech, Brno, Czechia, 5, 2021 | 131 | 2021 |
| End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study H Coppock, A Gaskell, P Tzirakis, A Baird, L Jones, B Schuller BMJ Innovations, bmjinnov-2021-000668, 2021 | 116 | 2021 |
| Sentiment Analysis and Topic Recognition in Video Transcriptions L Stappen, A Baird, E Cambria, BW Schuller IEEE Intelligent Systems 36, 2, 2021 | 108 | 2021 |
| Abaw: Valence-arousal estimation, expression recognition, action unit detection & emotional reaction intensity estimation challenges D Kollias, P Tzirakis, A Baird, A Cowen, S Zafeiriou Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 88 | 2023 |
| The MuSe 2021 Multimodal Sentiment Analysis Challenge: Sentiment, Emotion, Physiological-Emotion, and Stress L Stappen, A Baird, L Christ, L Schumann, B Sertolli, EM Messner, ... arXiv preprint arXiv:2104.07123, 2021 | 84 | 2021 |
| Emotional expression in psychiatric conditions: New technology for clinicians K Grabowski, A Rynkiewicz, A Lassalle, S Baron‐Cohen, B Schuller, ... Psychiatry and clinical neurosciences 73 (2), 50-62, 2019 | 84 | 2019 |
| StarGAN for Emotional Speech Conversion: Validated by Data Augmentation of End-To-End Emotion Recognition G Rizos, A Baird, M Elliott, B Schuller ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and …, 2020 | 71 | 2020 |
| MuSe 2020 Challenge and Workshop: Multimodal Sentiment Analysis, Emotion-target Engagement and Trustworthiness Detection in Real-life Media: Emotional Car Reviews in-the-wild L Stappen, A Baird, G Rizos, P Tzirakis, X Du, F Hafner, L Schumann, ... Proceedings of the 1st International on Multimodal Sentiment Analysis in …, 2020 | 70* | 2020 |
| The multimodal sentiment analysis in car reviews (muse-car) dataset: Collection, insights and improvements L Stappen, A Baird, L Schumann, B Schuller IEEE Transactions on Affective Computing 14 (2), 1334-1350, 2021 | 62 | 2021 |
| Active learning for bird sound classification via a kernel-based extreme learning machine K Qian, Z Zhang, A Baird, B Schuller The Journal of the Acoustical Society of America 142 (4), 1796-1804, 2017 | 56 | 2017 |
| The MuSe 2022 Multimodal Sentiment Analysis Challenge: Humor, Emotional Reactions, and Stress L Christ, S Amiriparian, A Baird, P Tzirakis, A Kathan, N Müller, L Stappen, ... | 52 | 2022 |
| MuSe-Toolbox: The Multimodal Sentiment Analysis Continuous Annotation Fusion and Discrete Class Transformation Toolbox L Stappen, L Schumann, B Sertolli, A Baird, B Weigell, E Cambria, ... Proceedings of the 2nd on Multimodal Sentiment Analysis Challenge, 75-82, 2021 | 45 | 2021 |
| Automatic classification of autistic child vocalisations: A novel database and results A Baird, S Amiriparian, N Cummins, AM Alcorn, A Batliner, ... International Speech Communication Association 2017, 849-853, 2017 | 44 | 2017 |
| Machine Listening for Heart Status Monitoring: Introducing and Benchmarking HSS - the Heart Sounds Shenzhen Corpus F Dong, K Qian, Z Ren, A Baird, X Li, Z Dai, B Dong, F Metze, ... IEEE journal of biomedical and health informatics, 2019 | 41 | 2019 |
Björn SchullerProfessor, Technische Universität München (TUM) / Imperial College London & CSO, audEERING在 tum.de 的电子邮件经过验证
