Publications
1108 results found
Ren Z, Chang Y, Bartl-Pokorny KD, et al., 2022, The Acoustic Dissection of Cough: Diving into Machine Listening-based COVID-19 Analysis and Detection
<jats:title>Abstract</jats:title><jats:sec><jats:title>Purpose</jats:title><jats:p>The coronavirus disease 2019 (COVID-19) has caused a crisis worldwide. Amounts of efforts have been made to prevent and control COVID-19’s transmission, from early screenings to vaccinations and treatments. Recently, due to the spring up of many automatic disease recognition applications based on machine listening techniques, it would be fast and cheap to detect COVID-19 from recordings of cough, a key symptom of COVID-19. To date, knowledge on the acoustic characteristics of COVID-19 cough sounds is limited, but would be essential for structuring effective and robust machine learning models. The present study aims to explore acoustic features for distinguishing COVID-19 positive individuals from COVID-19 negative ones based on their cough sounds.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>With the theory of computational paralinguistics, we analyse the acoustic correlates of COVID-19 cough sounds based on the COMPARE feature set, i. e., a standardised set of 6,373 acoustic higher-level features. Furthermore, we train automatic COVID-19 detection models with machine learning methods and explore the latent features by evaluating the contribution of all features to the COVID-19 status predictions.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The experimental results demonstrate that a set of acoustic parameters of cough sounds, e. g., statistical functionals of the root mean square energy and Mel-frequency cepstral coefficients, are relevant for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our automatic COVID-19 detection model performs significantly above chance level, i. e., at an unweighted average recall (UAR) of 0.632, on a data set consisting of 1,411 cough samples (COVID-19 positiv
Lefter I, Baird A, Stappen L, et al., 2022, A Cross-Corpus Speech-Based Analysis of Escalating Negative Interactions, FRONTIERS IN COMPUTER SCIENCE, Vol: 4
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- Citations: 1
Milling M, Bartl-Pokorny KD, Schuller BW, 2022, Investigating Automatic Speech Emotion Recognition for Children with Autism Spectrum Disorder in interactive intervention sessions with the social robot Kaspar
<jats:title>ABSTRACT</jats:title><jats:p>In this contribution, we present the analyses of vocalisation data recorded in the first observation round of the European Commission’s Erasmus Plus project “EMBOA, Affective loop in Socially Assistive Robotics as an intervention tool for children with autism”. In total, the project partners recorded data in 112 robot-supported intervention sessions for children with autism spectrum disorder. Audio data were recorded using the internal and lapel microphone of the H4n Pro Recorder. To analyse the data, we first utilise a child voice activity detection (VAD) system in order to extract child vocalisations from the raw audio data. For each child, session, and microphone, we provide the total time child vocalisations were detected. Next, we compare the results of two different implementations for valence- and arousal-based speech emotion recognition, thereby processing (1) the child vocalisations detected by the VAD and (2) the total recorded audio material. We provide average valence and arousal values for each session and condition. Finally, we discuss challenges and limitations of child voice detection and audio-based emotion recognition in robot-supported intervention settings.</jats:p>
Liu S, Han J, Puyal EL, et al., 2022, Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder, PATTERN RECOGNITION, Vol: 123, ISSN: 0031-3203
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- Citations: 11
Parada-Cabaleiro E, Batliner A, Baird A, et al., 2022, The perception of emotional cues by children in artificial background noise (vol 23, pg 169, 2020), INTERNATIONAL JOURNAL OF SPEECH TECHNOLOGY, Vol: 25, Pages: 289-289, ISSN: 1381-2416
Milling M, Baird A, Bartl-Pokorny KD, et al., 2022, Evaluating the Impact of Voice Activity Detection on Speech Emotion Recognition for Autistic Children, FRONTIERS IN COMPUTER SCIENCE, Vol: 4
Deshpande G, Batliner A, Schuller BW, 2022, AI-Based human audio processing for COVID-19: A comprehensive overview, PATTERN RECOGNITION, Vol: 122, ISSN: 0031-3203
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- Citations: 19
Mohamed MM, Nessiem MA, Batliner A, et al., 2022, Face mask recognition from audio: The MASC database and an overview on the mask challenge, PATTERN RECOGNITION, Vol: 122, ISSN: 0031-3203
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- Citations: 12
Schuller BW, Eldar Y, Pantic M, et al., 2022, Editorial: Intelligent Signal Analysis for Contagious Virus Diseases, IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, Vol: 16, Pages: 159-163, ISSN: 1932-4553
Chang Y, Jing X, Ren Z, et al., 2022, CovNet: A Transfer Learning Framework for Automatic COVID-19 Detection From Crowd-Sourced Cough Sounds, FRONTIERS IN DIGITAL HEALTH, Vol: 3
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- Citations: 2
Wen S, Huang T, Schuller BW, et al., 2022, Guest Editorial Introduction to the Special Section on Efficient Network Design for Convergence of Deep Learning and Edge Computing, IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING, Vol: 9, Pages: 109-110, ISSN: 2327-4697
Nessiem MA, Coppock H, Mohamed MM, et al., 2022, Artificial intelligence in COVID-19, Omics Approaches and Technologies in COVID-19, Pages: 255-273, ISBN: 9780323986212
The COVID-19 pandemic has taken the world by storm, placing healthcare systems around the globe under immense pressure. The exceptional circumstance has made the scientific community turn to artificial intelligence (AI), with hopes that AI techniques can be used in all aspects of combating the pandemic, whether it is in using AI to uncover sequences in the genomic code of the severe acute respiratory syndrome coronavirus (SARS-CoV-2) virus for the purposes of developing therapeutics, such as antivirals, antibodies, or vaccines, or using AI to provide (near-) instantaneous clinical diagnosis techniques by way of analysis of chest X-ray (CXR) images, computed tomography (CT) scans or other useful modalities, or using AI for as a tool for mass population testing by analyzing patient audio recordings. In this chapter, we survey the AI research literature with respect to applications for COVID-19 and showcase and critique notable state of the art approaches.
Milling M, Aslan I, Berghofer M, et al., 2022, Online Personalisation of Deep Mobile Activity Recognisers, 7th International Workshop on Sensor-Based Activity Recognition and Artificial Intelligence (IWOAR), Publisher: ASSOC COMPUTING MACHINERY
Zhao S, Huang Q, Tang Y, et al., 2022, Computational Emotion Analysis From Images: Recent Advances and Future Directions, Human Perception of Visual Information, Publisher: Springer International Publishing, Pages: 85-113, ISBN: 9783030814649
Xu X, Deng J, Cummins N, et al., 2022, Exploring Zero-Shot Emotion Recognition in Speech Using Semantic-Embedding Prototypes, IEEE TRANSACTIONS ON MULTIMEDIA, Vol: 24, Pages: 2752-2765, ISSN: 1520-9210
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- Citations: 6
Lu C, Zong Y, Zheng W, et al., 2022, Domain Invariant Feature Learning for Speaker-Independent Speech Emotion Recognition, IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, Vol: 30, Pages: 2217-2230, ISSN: 2329-9290
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- Citations: 10
Yang Z, Jing X, Triantafyllopoulos A, et al., 2022, An Overview & Analysis of Sequence-to-Sequence Emotional Voice Conversion, Interspeech Conference, Publisher: ISCA-INT SPEECH COMMUNICATION ASSOC, Pages: 4915-4919, ISSN: 2308-457X
Yan T, Meng H, Liu S, et al., 2022, CONVOLUATIONAL TRANSFORMER WITH ADAPTIVE POSITION EMBEDDING FOR COVID-19 DETECTION FROM COUGH SOUNDS, 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 9092-9096, ISSN: 1520-6149
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- Citations: 2
Jing X, Liu S, Parada-Cabaleiro E, et al., 2022, A Temporal-oriented Broadcast ResNet for COVID-19 Detection, 4th IEEE-EMBS International Conference on Wearable and Implantable Body Sensor Networks (BSN) / 18th IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Publisher: IEEE
Qian K, Schultz T, Schuller BW, 2022, AN OVERVIEW OF THE FIRST ICASSP SPECIAL SESSION ON COMPUTER AUDITION FOR HEALTHCARE, 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 9002-9006, ISSN: 1520-6149
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- Citations: 1
Yu S, Ding Y, Qian K, et al., 2022, A GLANCE-AND-GAZE NETWORK FOR RESPIRATORY SOUND CLASSIFICATION, 47th IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Publisher: IEEE, Pages: 9007-9011, ISSN: 1520-6149
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- Citations: 1
Kim JY, Liu C, Calvo RA, et al., 2022, Comparison of Automatic Speech Recognition Systems, International Workshop on Spoken Dialog System Technology, Publisher: Springer Nature Singapore, Pages: 123-131, ISSN: 1876-1100
Hledikova A, Woszczyk D, Acman A, et al., 2022, Data Augmentation for Dementia Detection in Spoken Language, Interspeech Conference, Publisher: ISCA-INT SPEECH COMMUNICATION ASSOC, Pages: 2858-2862, ISSN: 2308-457X
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- Citations: 1
Mira R, Haliassos A, Petridis S, et al., 2022, SVTS: Scalable Video-to-Speech Synthesis, Interspeech Conference, Publisher: ISCA-INT SPEECH COMMUNICATION ASSOC, Pages: 1836-1840, ISSN: 2308-457X
Baird A, Triantafyllopoulos A, Zaenkert S, et al., 2021, An Evaluation of Speech-Based Recognition of Emotional and Physiological Markers of Stress, FRONTIERS IN COMPUTER SCIENCE, Vol: 3
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- Citations: 4
Coppock H, Jones L, Kiskin I, et al., 2021, Bias and privacy in AI's cough-based COVID-19 recognition, LANCET DIGITAL HEALTH, Vol: 3, Pages: E761-E761
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- Citations: 1
Schaefer J, Milling M, Schuller BW, et al., 2021, Towards automatic airborne pollen monitoring: From commercial devices to operational by mitigating class-imbalance in a deep learning approach, SCIENCE OF THE TOTAL ENVIRONMENT, Vol: 796, ISSN: 0048-9697
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- Citations: 19
Qian K, Schmitt M, Zheng H, et al., 2021, Computer Audition for Fighting the SARS-CoV-2 Corona Crisis-Introducing the Multitask Speech Corpus for COVID-19, IEEE INTERNET OF THINGS JOURNAL, Vol: 8, Pages: 16035-16046, ISSN: 2327-4662
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- Citations: 7
Han J, Zhang Z, Mascolo C, et al., 2021, Deep Learning for Mobile Mental Health: Challenges and recent advances, IEEE SIGNAL PROCESSING MAGAZINE, Vol: 38, Pages: 96-105, ISSN: 1053-5888
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- Citations: 5
Schuller B, Baird A, Gebhard A, et al., 2021, New Avenues in Audio Intelligence: Towards Holistic Real-life Audio Understanding, TRENDS IN HEARING, Vol: 25, ISSN: 2331-2165
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- Citations: 1
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