Imperial College London
Portrait Picture

ProfessorBjoernSchuller

Faculty of Engineering, Department of Computing

Professor of Artificial Intelligence
 
 
 
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Contact

 

+44 (0)20 7594 8357bjoern.schuller Website

 
 
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Location

 

574Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

1107 results found

Wolf S, Schmitt M, Schuller B, 2021, A football player rating system, Journal of Sports Analytics, Vol: 6, Pages: 243-257, ISSN: 2215-020X

<jats:p>Association football (soccer) is the most popular sport in the world, resulting in a large economic interest from investors, team managers, and betting agencies. For this reason, a vast number of rating systems exists to assess the strength of football teams or individual players. Nevertheless, most of the existing approaches incorporate deficiencies, e. g., that they depend on subjective ratings from experts. The objective of this work was the development of a new rating system for determining the playing strength of football players. The Elo algorithm, which has established itself as an objective and adaptive rating system in numerous individual sports, has been expanded in accordance with the requirements of team sports. Matches from 16 different European domestic leagues, the UEFA Champions and Europa Leagues have been recorded, with more than 17 000 matches played in recent years, and 12 400 different players. The developed rating system produced promising results, when evaluating the matches based on its predictions. A high relevance of the created system results from the fact that only the associated match report is needed and thus—in relation to existing valuation models—significantly more football players can be assessed.</jats:p>

Journal article

Friedl K, Rizos G, Stappen L, Hasan M, Specia L, Hain T, Schuller BWet al., 2021, Uncertainty Aware Review Hallucination for Science Article Classification, Pages: 5004-5009

The high subjectivity and costs inherent in peer reviewing have recently motivated the preliminary design of machine learning-based acceptance decision methods. However, such approaches are limited in that they: a) do not explore the usage of both the reviewer and area chair recommendations, b) do not explicitly model subjectivity on a per submission basis, and c) are not applicable in realistic settings, by assuming that review texts are available at test time, when these are exactly the inputs that should be considered to be missing in this application. We propose to utilise methods that model the aleatory uncertainty of the submissions, while also exploring different loss importance interpolations between area chair and reviewers' recommendations. We also propose a modality hallucination approach to impute review representations at test time, providing the first realistic evaluation framework for this challenging task.

Conference paper

Han J, Zhang Z, Pantic M, Schuller Bet al., 2021, Internet of emotional people: Towards continual affective computing cross cultures via audiovisual signals, FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, Vol: 114, Pages: 294-306, ISSN: 0167-739X

Journal article

Schuller DM, Schuller BW, 2021, A Review on Five Recent and Near-Future Developments in Computational Processing of Emotion in the Human Voice, EMOTION REVIEW, Vol: 13, Pages: 44-50, ISSN: 1754-0739

Journal article

Aspandi D, Sukno F, Schuller B, Binefa Xet al., 2021, An Enhanced Adversarial Network with Combined Latent Features for Spatio-temporal Facial Affect Estimation in the Wild, 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) / 16th International Conference on Computer Vision Theory and Applications (VISAPP), Publisher: SCITEPRESS, Pages: 172-181

Conference paper

Qian K, Schuller BW, Yamamoto Y, 2021, Recent Advances in Computer Audition for Diagnosing COVID-19: An Overview, 2021 IEEE 3RD GLOBAL CONFERENCE ON LIFE SCIENCES AND TECHNOLOGIES (IEEE LIFETECH 2021), Pages: 181-182

Journal article

Li C, Zhang Q, Zhao Z, Gu L, Schuller Bet al., 2021, Exploring Spatial-Temporal Representations for fNIRS-based Intimacy Detection via an Attention-enhanced Cascade Convolutional Recurrent Neural Network, 25th International Conference on Pattern Recognition (ICPR), Publisher: IEEE COMPUTER SOC, Pages: 8862-8869, ISSN: 1051-4651

Conference paper

Tzirakis P, Anh N, Zafeiriou S, Schuller BWet al., 2021, SPEECH EMOTION RECOGNITION USING SEMANTIC INFORMATION, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 6279-6283

Conference paper

Haque KN, Rana R, Liu J, Hansen JHL, Cummins N, Busso C, Schuller BWet al., 2021, Guided Generative Adversarial Neural Network for Representation Learning and Audio Generation Using Fewer Labelled Audio Data, IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, Vol: 29, Pages: 2575-2590, ISSN: 2329-9290

Journal article

Ren Z, Kong Q, Han J, Plumbley MD, Schuller BWet al., 2021, CAA-Net: Conditional Atrous CNNs With Attention for Explainable Device-Robust Acoustic Scene Classification, IEEE TRANSACTIONS ON MULTIMEDIA, Vol: 23, Pages: 4131-4142, ISSN: 1520-9210

Journal article

Ma P, Mira R, Petridis S, Schuller BW, Pantic Met al., 2021, LiRA: Learning Visual Speech Representations from Audio through Self-supervision, Interspeech Conference, Publisher: ISCA-INT SPEECH COMMUNICATION ASSOC, Pages: 3011-3015, ISSN: 2308-457X

Conference paper

Li C, Chen B, Zhao Z, Cummins N, Schuller BWet al., 2021, HIERARCHICAL ATTENTION-BASED TEMPORAL CONVOLUTIONAL NETWORKS FOR EEG-BASED EMOTION RECOGNITION, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 1240-1244

Conference paper

Ye ZJ, Schuller BW, 2021, Deep Learning Post-Earnings-Announcement Drift, International Joint Conference on Neural Networks (IJCNN), Publisher: IEEE, ISSN: 2161-4393

Conference paper

Zhang R, Shi Y, Schuller B, Andre E, Oviatt S, Quigley A, Marquardt N, Asian I, Ju Ret al., 2021, User Experience for Multi-Device Ecosystems: Challenges and Opportunities, CHI Conference on Human Factors in Computing Systems, Publisher: ASSOC COMPUTING MACHINERY

Conference paper

Mallol-Ragolta A, Liu S, Schuller BW, 2021, The Filtering Effect of Face Masks in their Detection from Speech, 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC), Publisher: IEEE, Pages: 2079-2082, ISSN: 1557-170X

Conference paper

Koike T, Qian K, Schuller BW, Yamamoto Yet al., 2021, Transferring Cross-Corpus Knowledge: An Investigation on Data Augmentation for Heart Sound Classification, 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC), Publisher: IEEE, Pages: 1976-1979, ISSN: 1557-170X

Conference paper

Chang Y, Ren Z, Schuller BW, 2021, Transformer-based CNNs: Mining Temporal Context Information for Multi-sound COVID-19 Diagnosis, 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC), Publisher: IEEE, Pages: 2335-2338, ISSN: 1557-170X

Conference paper

Qian K, Koike T, Tamada K, Takumi T, Schuller BW, Yamamoto Yet al., 2021, Sensing the Sounds of Silence: A Pilot Study on the Detection of Model Mice of Autism Spectrum Disorder from Ultrasonic Vocalisations, 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC), Publisher: IEEE, Pages: 68-71, ISSN: 1557-170X

Conference paper

Karas V, Schuller BW, 2021, Deep Learning for Sentiment Analysis, Advances in Business Information Systems and Analytics, Publisher: IGI Global, Pages: 97-132, ISBN: 9781799842408

<jats:p>Sentiment analysis is an important area of natural language processing that can help inform business decisions by extracting sentiment information from documents. The purpose of this chapter is to introduce the reader to selected concepts and methods of deep learning and show how deep models can be used to increase performance in sentiment analysis. It discusses the latest advances in the field and covers topics including traditional sentiment analysis approaches, the fundamentals of sentence modelling, popular neural network architectures, autoencoders, attention modelling, transformers, data augmentation methods, the benefits of transfer learning, the potential of adversarial networks, and perspectives on explainable AI. The authors' intent is that through this chapter, the reader can gain an understanding of recent developments in this area as well as current trends and potentials for future research.</jats:p>

Book chapter

Rajamani ST, Rajamani K, Schuller BW, 2021, Towards an Efficient Deep Learning Model for Emotion and Theme Recognition in Music, 23rd IEEE International Workshop on Multimedia Signal Processing (IEEE MMSP), Publisher: IEEE, ISSN: 2163-3517

Conference paper

Hecker P, Pokorny FB, Bartl-Pokorny KD, Reichel U, Ren Z, Hantke S, Eyben F, Schuller DM, Arnrich B, Schuller BWet al., 2021, Speaking Corona? Human and Machine Recognition of COVID-19 from Voice, Interspeech Conference, Publisher: ISCA-INT SPEECH COMMUNICATION ASSOC, Pages: 1029-1033, ISSN: 2308-457X

Conference paper

Yan T, Meng H, Parada-Cabaleiro E, Liu S, Song M, Schuller BWet al., 2021, Coughing-based Recognition of Covid-19 with Spatial Attentive ConvLSTM Recurrent Neural Networks, Interspeech Conference, Publisher: ISCA-INT SPEECH COMMUNICATION ASSOC, Pages: 4154-4158, ISSN: 2308-457X

Conference paper

Mallol-Ragolta A, Cuesta H, Gomez E, Schuller BWet al., 2021, Cough-based COVID-19 Detection with Contextual Attention Convolutional Neural Networks and Gender Information, Interspeech Conference, Publisher: ISCA-INT SPEECH COMMUNICATION ASSOC, Pages: 941-945, ISSN: 2308-457X

Conference paper

Karas V, Schuller BW, 2021, Recognising Covid-19 from Coughing using Ensembles of SVMs and LSTMs with Handcrafted and Deep Audio Features, Interspeech Conference, Publisher: ISCA-INT SPEECH COMMUNICATION ASSOC, Pages: 911-915, ISSN: 2308-457X

Conference paper

Deshpande G, Schuller BW, 2021, The DiCOVA 2021 Challenge - An Encoder-Decoder Approach for COVID-19 Recognition from Coughing Audio, Interspeech Conference, Publisher: ISCA-INT SPEECH COMMUNICATION ASSOC, Pages: 931-935, ISSN: 2308-457X

Conference paper

Bartl-Pokorny KD, Pykala M, Uluer P, Barkana DE, Baird A, Kose H, Zorcec T, Robins B, Schuller BW, Landowska Aet al., 2021, Robot-Based Intervention for Children With Autism Spectrum Disorder: A Systematic Literature Review, IEEE ACCESS, Vol: 9, Pages: 165433-165450, ISSN: 2169-3536

Journal article

Cheng J, Liang R, Liang Z, Zhao L, Huang C, Schuller Bet al., 2021, A Deep Adaptation Network for Speech Enhancement: Combining a Relativistic Discriminator With Multi-Kernel Maximum Mean Discrepancy, IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, Vol: 29, Pages: 41-53, ISSN: 2329-9290

Journal article

Nessiem MA, Mohamed MM, Coppock H, Gaskell A, Schuller BWet al., 2021, Detecting COVID-19 from Breathing and Coughing Sounds using Deep Neural Networks, 2021 IEEE 34TH INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS (CBMS), Pages: 183-188, ISSN: 2372-9198

Journal article

Narendra NP, Schuller B, Alku P, 2021, The Detection of Parkinson's Disease From Speech Using Voice Source Information, IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, Vol: 29, Pages: 1925-1936, ISSN: 2329-9290

Journal article

Baird A, Amiriparian S, Milling M, Schuller BWet al., 2021, EMOTION RECOGNITION IN PUBLIC SPEAKING SCENARIOS UTILISING AN LSTM-RNN APPROACH WITH ATTENTION, IEEE Spoken Language Technology Workshop (SLT), Publisher: IEEE, Pages: 397-402, ISSN: 2639-5479

Conference paper

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