Publications
949 results found
Zhang Z, Qian K, Schuller BW, et al., 2021, An Online Robot Collision Detection and Identification Scheme by Supervised Learning and Bayesian Decision Theory, IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, Vol: 18, Pages: 1144-1156, ISSN: 1545-5955
Sertolli B, Ren Z, Schuller BW, et al., 2021, Representation transfer learning from deep end -to -end speech recognition networks for the classi fi cation of health states from speech, COMPUTER SPEECH AND LANGUAGE, Vol: 68, ISSN: 0885-2308
Özbay AG, Hamzehloo A, Laizet S, et al., 2021, Poisson CNN: Convolutional neural networks for the solution of the Poisson equation on a Cartesian mesh, Data-Centric Engineering, Vol: 2, Pages: 1-31, ISSN: 2632-6736
<jats:title>Abstract</jats:title> <jats:p>The Poisson equation is commonly encountered in engineering, for instance, in computational fluid dynamics (CFD) where it is needed to compute corrections to the pressure field to ensure the incompressibility of the velocity field. In the present work, we propose a novel fully convolutional neural network (CNN) architecture to infer the solution of the Poisson equation on a 2D Cartesian grid with different resolutions given the right-hand side term, arbitrary boundary conditions, and grid parameters. It provides unprecedented versatility for a CNN approach dealing with partial differential equations. The boundary conditions are handled using a novel approach by decomposing the original Poisson problem into a homogeneous Poisson problem plus four inhomogeneous Laplace subproblems. The model is trained using a novel loss function approximating the continuous <jats:inline-formula> <jats:alternatives> <jats:inline-graphic xmlns:xlink="http://www.w3.org/1999/xlink" mime-subtype="png" xlink:href="S2632673621000071_inline1.png" /> <jats:tex-math>$ {L}^p $</jats:tex-math> </jats:alternatives> </jats:inline-formula> norm between the prediction and the target. Even when predicting on grids denser than previously encountered, our model demonstrates encouraging capacity to reproduce the correct solution profile. The proposed model, which outperforms well-known neural network models, can be included in a CFD solver to help with solving the Poisson equation. Analytical test cases indicate that our CNN architecture is capable of predicting the correct solution of a Poisson problem with mean percentage errors below 10%, an improvement by comparison to the first step of conventional iterative methods. Predictions from our model, used as the initial guess to iterative algorithms like Multigrid, can reduce the root mean square error af
Zhang L, Qian K, Schuller BW, et al., 2021, Prediction on Mechanical Properties of Non-Equiatomic High-Entropy Alloy by Atomistic Simulation and Machine Learning, METALS, Vol: 11
Qian K, Koike T, Yoshiuchi K, et al., 2021, Can Appliances Understand the Behavior of Elderly Via Machine Learning? A Feasibility Study, IEEE INTERNET OF THINGS JOURNAL, Vol: 8, Pages: 8343-8355, ISSN: 2327-4662
Tzirakis P, Chen J, Zafeiriou S, et al., 2021, End-to-end multimodal affect recognition in real-world environments, INFORMATION FUSION, Vol: 68, Pages: 46-53, ISSN: 1566-2535
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- Citations: 3
Qian K, Janott C, Schmitt M, et al., 2021, Can Machine Learning Assist Locating the Excitation of Snore Sound? A Review, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 25, Pages: 1233-1246, ISSN: 2168-2194
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- Citations: 1
Coppock H, Gaskell A, Tzirakis P, et al., 2021, End-to-end convolutional neural network enables COVID-19 detection from breath and cough audio: a pilot study., BMJ Innov, Vol: 7, Pages: 356-362, ISSN: 2055-642X
Background: Since the emergence of COVID-19 in December 2019, multidisciplinary research teams have wrestled with how best to control the pandemic in light of its considerable physical, psychological and economic damage. Mass testing has been advocated as a potential remedy; however, mass testing using physical tests is a costly and hard-to-scale solution. Methods: This study demonstrates the feasibility of an alternative form of COVID-19 detection, harnessing digital technology through the use of audio biomarkers and deep learning. Specifically, we show that a deep neural network based model can be trained to detect symptomatic and asymptomatic COVID-19 cases using breath and cough audio recordings. Results: Our model, a custom convolutional neural network, demonstrates strong empirical performance on a data set consisting of 355 crowdsourced participants, achieving an area under the curve of the receiver operating characteristics of 0.846 on the task of COVID-19 classification. Conclusion: This study offers a proof of concept for diagnosing COVID-19 using cough and breath audio signals and motivates a comprehensive follow-up research study on a wider data sample, given the evident advantages of a low-cost, highly scalable digital COVID-19 diagnostic tool.
Xu X, Deng J, Zhang Z, et al., 2021, Identifying surgical-mask speech using deep neural networks on low-level aggregation, Pages: 580-585
The task of Mask-Speech Identification (MSI) aims at judging whether a chunk of speech is pronounced when the speaker is wearing a facial mask or not. Most of the existing related research focuses on investigating the influence of wearing a mask, which only adapts in some certain cases to speech analysis. Thus in order to generalise the research on MSI, we propose an MSI approach using deep networks on Low-Level Aggregation (LLA) for speech chunks. The proposed approach benefits from data augmentation on Low-Level Descriptors (LLDs), resulting in more adaptation to deep models through inputting much more samples in training without employing pre-trained knowledge. Experiments are performed on the dataset of Mask Augsburg Speech Corpus (MSC) used in the INTERSPEECH 2020 ComParE challenge, considering the influence from employing different strategies. The experimental results show effectiveness of the proposed approach compared with the ComParE challenge baselines.
Kossaifi J, Walecki R, Panagakis Y, et al., 2021, SEWA DB: A rich database for audio-visual emotion and sentiment research in the wild, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol: 43, Pages: 1022-1040, ISSN: 0162-8828
Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices are becoming indispensable part of our life more and more. Accurately annotated real-world data are the crux in devising such systems. However, existing databases usually consider controlled settings, low demographic variability, and a single task. In this paper, we introduce the SEWA database of more than 2000 minutes of audio-visual data of 398 people coming from six cultures, 50% female, and uniformly spanning the age range of 18 to 65 years old. Subjects were recorded in two different contexts: while watching adverts and while discussing adverts in a video chat. The database includes rich annotations of the recordings in terms of facial landmarks, facial action units (FAU), various vocalisations, mirroring, and continuously valued valence, arousal, liking, agreement, and prototypic examples of (dis)liking. This database aims to be an extremely valuable resource for researchers in affective computing and automatic human sensing and is expected to push forward the research in human behaviour analysis, including cultural studies. Along with the database, we provide extensive baseline experiments for automatic FAU detection and automatic valence, arousal and (dis)liking intensity estimation.
Stappen L, Baird A, Cambria E, et al., 2021, Sentiment Analysis and Topic Recognition in Video Transcriptions, IEEE Intelligent Systems, Vol: 36, Pages: 88-95, ISSN: 1541-1672
Haque KN, Rana R, Liu J, et 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
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
Cheng J, Liang R, Liang Z, et 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
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- Citations: 1
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
Baird A, Amiriparian S, Milling M, et 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
Han J, Zhang Z, Pantic M, et 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
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- Citations: 1
Amiriparian S, Gerczuk M, Ottl S, et al., 2020, Towards cross-modal pre-training and learning tempo-spatial characteristics for audio recognition with convolutional and recurrent neural networks, EURASIP JOURNAL ON AUDIO SPEECH AND MUSIC PROCESSING, Vol: 2020, ISSN: 1687-4722
Pandit V, Schmitt M, Cummins N, et al., 2020, I see it in your eyes: Training the shallowest-possible CNN to recognise emotions and pain from muted web-assisted in-the-wild video-chats in real-time, INFORMATION PROCESSING & MANAGEMENT, Vol: 57, ISSN: 0306-4573
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- Citations: 2
Baird A, Schuller B, 2020, Considerations for a More Ethical Approach to Data in AI: On Data Representation and Infrastructure, FRONTIERS IN BIG DATA, Vol: 3
Zhang Z, Metaxas DN, Lee H-Y, et al., 2020, Guest Editorial Special Issue on Adversarial Learning in Computational Intelligence, IEEE Transactions on Emerging Topics in Computational Intelligence, Vol: 4, Pages: 414-416
Dong F, Qian K, Ren Z, et al., 2020, Machine Listening for Heart Status Monitoring: Introducing and Benchmarking HSS-The Heart Sounds Shenzhen Corpus, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 24, Pages: 2082-2092, ISSN: 2168-2194
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- Citations: 1
Han J, Zhang Z, Ren Z, et al., 2020, Exploring Perception Uncertainty for Emotion Recognition in Dyadic Conversation and Music Listening, COGNITIVE COMPUTATION, Vol: 13, Pages: 231-240, ISSN: 1866-9956
Parada-Cabaleiro E, Costantini G, Batliner A, et al., 2020, DEMoS: an Italian emotional speech corpus Elicitation methods, machine learning, and perception, LANGUAGE RESOURCES AND EVALUATION, Vol: 54, Pages: 341-383, ISSN: 1574-020X
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- Citations: 2
Amiriparian S, Cummins N, Gerczuk M, et al., 2020, "Are You Playing a Shooter Again?!" Deep Representation Learning for Audio-Based Video Game Genre Recognition, IEEE TRANSACTIONS ON GAMES, Vol: 12, Pages: 145-154, ISSN: 2475-1502
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- Citations: 2
Schuller DM, Schuller BW, 2020, 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
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- Citations: 5
Kaklauskas A, Zavadskas EK, Schuller B, et al., 2020, Customized ViNeRS Method for Video Neuro-Advertising of Green Housing, INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH, Vol: 17
Wu P, Sun X, Zhao Z, et al., 2020, Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning, COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, Vol: 2020, ISSN: 1687-5265
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- Citations: 4
Pokorny FB, Bartl-Pokorny KD, Zhang D, et al., 2020, Efficient Collection and Representation of Preverbal Data in Typical and Atypical Development, JOURNAL OF NONVERBAL BEHAVIOR, Vol: 44, Pages: 419-436, ISSN: 0191-5886
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- Citations: 2
Deng J, Schuller B, Eyben F, et al., 2020, Exploiting time-frequency patterns with LSTM-RNNs for low-bitrate audio restoration, NEURAL COMPUTING & APPLICATIONS, Vol: 32, Pages: 1095-1107, ISSN: 0941-0643
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- Citations: 4
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