Schuller, Björn

Opensmile: the munich versatile and fast open-source audio feature extractor

Eyben, F., Wöllmer, M., & Schuller, B. (2010) · Proceedings of the 18th ACM international conference on Multimedia

Music and Audio ProcessingSpeech and Audio Processing

Cited by 2,642

The INTERSPEECH 2009 emotion challenge

Schuller, B., Steidl, S., & Batliner, A. (2009) · Interspeech 2009

Emotion and Mood RecognitionSpeech Recognition and Synthesis

Cited by 938

End-to-End Multimodal Emotion Recognition using Deep Neural Networks

Tzirakis, P., Trigeorgis, G., Nicolaou, M. A., Schuller, B., & Zafeiriou, S. (2017) · arXiv

Emotion and Mood RecognitionSpeech and Audio Processing

Cited by 752

AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition

Ringeval, F., Schuller, B., Valstar, M., Cummins, N., Cowie, R., Tavabi, L., Schmitt, M., Alisamir, S., Amiriparian, S., Messner, E.-M., et al. (2019) · Proceedings of the 9th International on Audio/Visual Emotion Challenge and Workshop

Emotion and Mood RecognitionSpeech and Audio Processing

Cited by 409

An investigation of the ‘female camouflage effect’ in autism using a computerized ADOS-2 and a test of sex/gender differences

Rynkiewicz, A., Schuller, B., Marchi, E., Piana, S., Camurri, A., Lassalle, A., & Baron-Cohen, S. (2016) · Molecular Autism

Autism Spectrum Disorder ResearchSocial Robot Interaction and HRI

Cited by 404

Categorical and dimensional affect analysis in continuous input: Current trends and future directions

Gunes, H., & Schuller, B. (2013) · Image and Vision Computing

Emotion and Mood RecognitionSentiment Analysis and Opinion Mining

Cited by 360

LSTM-Modeling of continuous emotions in an audiovisual affect recognition framework

Wöllmer, M., Kaiser, M., Eyben, F., Schuller, B., & Rigoll, G. (2013) · Image and Vision Computing

Emotion and Mood RecognitionSpeech and Audio Processing

Cited by 288

The TUM Gait from Audio, Image and Depth (GAID) database: Multimodal recognition of subjects and traits

Hofmann, M., Geiger, J., Bachmann, S., Schuller, B., & Rigoll, G. (2014) · Journal of Visual Communication and Image Representation

Gait Recognition and AnalysisFace recognition and analysis

Cited by 246

The INTERSPEECH 2012 speaker trait challenge

Schuller, B., Steidl, S., Batliner, A., Nöth, E., Vinciarelli, A., Burkhardt, F., Son, R. V., Weninger, F., Eyben, F., Bocklet, T., et al. (2012) · Interspeech 2012

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 211

Prediction of asynchronous dimensional emotion ratings from audiovisual and physiological data

Ringeval, F., Eyben, F., Kroupi, E., Yuce, A., Thiran, J.-P., Ebrahimi, T., Lalanne, D., & Schuller, B. (2015) · Pattern Recognition Letters

Emotion and Mood RecognitionSentiment Analysis and Opinion Mining

Cited by 202

Context-sensitive multimodal emotion recognition from speech and facial expression using bidirectional LSTM modeling

Wöllmer, M., Metallinou, A., Eyben, F., Schuller, B., & Narayanan, S. S. (2010) · Interspeech 2010

Emotion and Mood RecognitionSpeech and Audio Processing

Cited by 201

Building autonomous sensitive artificial listeners

Schroder, M., Bevacqua, E., Cowie, R., Eyben, F., Gunes, H., Heylen, D., ter Maat, M., McKeown, G., Pammi, S., Pantic, M., et al. (2012) · IEEE Transactions on Affective Computing

Speech and dialogue systemsEmotion and Mood Recognition

Cited by 185

Will Affective Computing Emerge From Foundation Models and General Artificial Intelligence? A First Evaluation of ChatGPT

Amin, M. M., Cambria, E., & Schuller, B. W. (2023) · IEEE Intelligent Systems

Sentiment Analysis and Opinion MiningEmotion and Mood Recognition

Cited by 162

Combining Efforts for Improving Automatic Classification of Emotional User States

Batliner, A., Steidl, S., Schuller, B., Seppi, D., Laskowski, K., Vogt, T., Devillers, L., Vidrascu, L., Amir, N., Kessous, L., et al. (2006) · Jožef Stefan Institute, Ljubljana

Emotion and Mood RecognitionSentiment Analysis and Opinion Mining

Cited by 119

End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study

Coppock, H., Gaskell, A., Tzirakis, P., Baird, A., Jones, L., & Schuller, B. (2021) · BMJ Innovations

Phonocardiography and Auscultation TechniquesCOVID-19 diagnosis using AI

Cited by 108

End-to-end multimodal affect recognition in real-world environments

Tzirakis, P., Chen, J., Zafeiriou, S., & Schuller, B. (2021) · Information Fusion

Emotion and Mood RecognitionSentiment Analysis and Opinion Mining

Cited by 105

Contrastive Learning Based Modality-Invariant Feature Acquisition for Robust Multimodal Emotion Recognition With Missing Modalities

Liu, R., Zuo, H., Lian, Z., Schuller, B. W., & Li, H. (2024) · IEEE Transactions on Affective Computing

Emotion and Mood RecognitionSentiment Analysis and Opinion Mining

Cited by 96

The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates

Schuller, B. W., Batliner, A., Bergler, C., Mascolo, C., Han, J., Lefter, I., Kaya, H., Amiriparian, S., Baird, A., Stappen, L., et al. (2021) · Interspeech 2021

Speech Recognition and SynthesisEmotion and Mood Recognition

Cited by 89

Voice Analysis for Neurological Disorder Recognition–A Systematic Review and Perspective on Emerging Trends

Hecker, P., Steckhan, N., Eyben, F., Schuller, B. W., & Arnrich, B. (2022) · Frontiers in Digital Health

Voice and Speech DisordersEmotion and Mood Recognition

Cited by 78

Validity of machine learning in biology and medicine increased through collaborations across fields of expertise

Littmann, M., Selig, K., Cohen-Lavi, L., Frank, Y., Hönigschmid, P., Kataka, E., Mösch, A., Qian, K., Ron, A., Schmid, S., et al. (2020) · Nature Machine Intelligence

Interdisciplinary Research and Collaborationscientometrics and bibliometrics research

Cited by 70

Applying multiple classifiers and non-linear dynamics features for detecting sleepiness from speech

Krajewski, J., Schnieder, S., Sommer, D., Batliner, A., & Schuller, B. (2012) · Neurocomputing

Sleep and Work-Related FatigueEmotion and Mood Recognition

Cited by 67

HEAR: Holistic Evaluation of Audio Representations

Turian, J., Shier, J., Khan, H. R., Raj, B., Schuller, B. W., Steinmetz, C. J., Malloy, C., Tzanetakis, G., Velarde, G., McNally, K., et al. (2022) · arXiv

Music and Audio ProcessingSpeech Recognition and Synthesis

Cited by 56

COVID-19 detection from audio: seven grains of salt

Coppock, H., Jones, L., Kiskin, I., & Schuller, B. (2021) · The Lancet Digital Health

Phonocardiography and Auscultation TechniquesCOVID-19 diagnosis using AI

Cited by 55

Strength modelling for real-worldautomatic continuous affect recognition from audiovisual signals

Han, J., Zhang, Z., Cummins, N., Ringeval, F., & Schuller, B. (2017) · Image and Vision Computing

Emotion and Mood RecognitionSpeech and Audio Processing

Cited by 53