Publications
1107 results found
Schmid J, Hoss A, Schuller BW, 2022, A Survey on Client Throughput Prediction Algorithms in Wired and Wireless Networks, ACM COMPUTING SURVEYS, Vol: 54, ISSN: 0360-0300
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- Citations: 2
Schuller BW, Loechner J, Qian K, et al., 2022, Digital Mental Health-Breaking a Lance for Prevention, IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, Vol: 9, Pages: 1584-1588, ISSN: 2329-924X
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- Citations: 2
Bartl-Pokorny KD, Pokorny FB, Garrido D, et al., 2022, Vocalisation Repertoire at the End of the First Year of Life: An Exploratory Comparison of Rett Syndrome and Typical Development, JOURNAL OF DEVELOPMENTAL AND PHYSICAL DISABILITIES, Vol: 34, Pages: 1053-1069, ISSN: 1056-263X
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- Citations: 2
Kathan A, Harrer M, Kuester L, et al., 2022, Personalised depression forecasting using mobile sensor data and ecological momentary assessment, FRONTIERS IN DIGITAL HEALTH, Vol: 4
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- Citations: 2
Loechner JW, Schuller B, 2022, Child and Youth Affective Computing-Challenge Accepted, IEEE INTELLIGENT SYSTEMS, Vol: 37, Pages: 69-76, ISSN: 1541-1672
Niu M, Zhao Z, Tao J, et al., 2022, Selective Element and Two Orders Vectorization Networks for Automatic Depression Severity Diagnosis via Facial Changes, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, Vol: 32, Pages: 8065-8077, ISSN: 1051-8215
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- Citations: 2
Mehta Y, Stachl C, Markov K, et al., 2022, Future-generation personality prediction from digital footprints, FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, Vol: 136, Pages: 322-325, ISSN: 0167-739X
Zhao S, Yao X, Yang J, et al., 2022, Affective Image Content Analysis: Two Decades Review and New Perspectives, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, Vol: 44, Pages: 6729-6751, ISSN: 0162-8828
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- Citations: 24
Hu B, Qian K, Dong Q, et al., 2022, Psychological Field Versus Physiological Field: From Qualitative Analysis to Quantitative Modeling of the Mental Status, IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, Vol: 9, Pages: 1275-1281, ISSN: 2329-924X
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- Citations: 2
Xu X, Deng J, Zhang Z, et al., 2022, Rethinking Auditory Affective Descriptors Through Zero-Shot Emotion Recognition in Speech, IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, Vol: 9, Pages: 1530-1541, ISSN: 2329-924X
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- Citations: 3
Hu B, Qian K, Zhang Y, et al., 2022, The Inverse Problems for Computational Psychophysiology: Opinions and Insights, CYBORG AND BIONIC SYSTEMS, Vol: 2022
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- Citations: 1
Ottl S, Amiriparian S, Gerczuk M, et al., 2022, motilitAl: A machine learning framework for automatic prediction of human sperm motility, ISCIENCE, Vol: 25
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- Citations: 6
Pokorny FB, Schmitt M, Egger M, et al., 2022, Automatic vocalisation-based detection of fragile X syndrome and Rett syndrome, SCIENTIFIC REPORTS, Vol: 12, ISSN: 2045-2322
Liu S, Mallol-Ragolta A, Yan T, et al., 2022, Capturing Time Dynamics From Speech Using Neural Networks for Surgical Mask Detection, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 26, Pages: 4291-4302, ISSN: 2168-2194
Qian K, Koike T, Nakamura T, et al., 2022, Learning Multimodal Representations for Drowsiness Detection, IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, Vol: 23, Pages: 11539-11548, ISSN: 1524-9050
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- Citations: 5
Schuller BW, Lochner J, Qian K, et al., 2022, COVID-19's Impact on Mental Health-The Hour of Computational Aid?, IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, Vol: 9, Pages: 967-973, ISSN: 2329-924X
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- Citations: 2
Schuller BW, Batliner A, Amiriparian S, et al., 2022, The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes, Publisher: ArXuc
The ACM Multimedia 2022 Computational Paralinguistics Challenge addressesfour different problems for the first time in a research competition underwell-defined conditions: In the Vocalisations and Stuttering Sub-Challenges, aclassification on human non-verbal vocalisations and speech has to be made; theActivity Sub-Challenge aims at beyond-audio human activity recognition fromsmartwatch sensor data; and in the Mosquitoes Sub-Challenge, mosquitoes need tobe detected. We describe the Sub-Challenges, baseline feature extraction, andclassifiers based on the usual ComPaRE and BoAW features, the auDeep toolkit,and deep feature extraction from pre-trained CNNs using the DeepSpectRumtoolkit; in addition, we add end-to-end sequential modelling, and alog-mel-128-BNN.
Ren Z, Chang Y, Nejdl W, et al., 2022, Learning complementary representations via attention-based ensemble learning for cough-based COVID-19 recognition, ACTA ACUSTICA, Vol: 6
Hecker P, Steckhan N, Eyben F, et al., 2022, Voice Analysis for Neurological Disorder Recognition-A Systematic Review and Perspective on Emerging Trends, FRONTIERS IN DIGITAL HEALTH, Vol: 4
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- Citations: 8
Akman A, Coppock H, Gaskell A, et al., 2022, Evaluating the COVID-19 identification ResNet (CIdeR) on the INTERSPEECH COVID-19 from audio challenges, Frontiers in Digital Health, Vol: 4, ISSN: 2673-253X
Several machine learning-based COVID-19 classifiers exploiting vocal biomarkers of COVID-19 has been proposed recently as digital mass testing methods. Although these classifiers have shown strong performances on the datasets on which they are trained, their methodological adaptation to new datasets with different modalities has not been explored. We report on cross-running the modified version of recent COVID-19 Identification ResNet (CIdeR) on the two Interspeech 2021 COVID-19 diagnosis from cough and speech audio challenges: ComParE and DiCOVA. CIdeR is an end-to-end deep learning neural network originally designed to classify whether an individual is COVID-19-positive or COVID-19-negative based on coughing and breathing audio recordings from a published crowdsourced dataset. In the current study, we demonstrate the potential of CIdeR at binary COVID-19 diagnosis from both the COVID-19 Cough and Speech Sub-Challenges of INTERSPEECH 2021, ComParE and DiCOVA. CIdeR achieves significant improvements over several baselines. We also present the results of the cross dataset experiments with CIdeR that show the limitations of using the current COVID-19 datasets jointly to build a collective COVID-19 classifier.
Batliner A, Hantke S, Schuller B, 2022, Ethics and Good Practice in Computational Paralinguistics, IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, Vol: 13, Pages: 1236-1253, ISSN: 1949-3045
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- Citations: 5
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., J Voice
OBJECTIVES: 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 of 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. METHODS: By applying conventional inferential statistics, we analyze the acoustic correlates of COVID-19 cough sounds based on the ComParE feature set, i.e., a standardized 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. RESULTS: 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, bear essential acoustic information in terms of effect sizes for the differentiation between COVID-19 positive and COVID-19 negative cough samples. Our general 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 positive/negative: 210/1,201). CONCLUSIONS: Based on the acoustic correlates analysis on the ComParE feature set and the feature analysis in the effective COVID-19 detection approach, we find that several acoustic features that show higher effects in convention
Milling M, Pokorny FBB, Bartl-Pokorny KDD, et al., 2022, Is Speech the New Blood? Recent Progress in AI-Based Disease Detection From Audio in a Nutshell, FRONTIERS IN DIGITAL HEALTH, Vol: 4
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- Citations: 1
Latif S, Rana R, Khalifa S, et al., 2022, Multi-Task Semi-Supervised Adversarial Autoencoding for Speech Emotion Recognition, IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, Vol: 13, Pages: 992-1004, ISSN: 1949-3045
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- Citations: 25
Zhang Y, Weninger F, Schuller B, et al., 2022, Holistic Affect Recognition Using PaNDA: Paralinguistic Non-Metric Dimensional Analysis, IEEE TRANSACTIONS ON AFFECTIVE COMPUTING, Vol: 13, Pages: 769-780, ISSN: 1949-3045
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- Citations: 2
Zhang L, Li J, Li P, et al., 2022, MEDAS: an open-source platform as a service to help break the walls between medicine and informatics, NEURAL COMPUTING & APPLICATIONS, Vol: 34, Pages: 6547-6567, ISSN: 0941-0643
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- Citations: 2
Stappen L, Baird A, Lienhart M, et al., 2022, An Estimation of Online Video User Engagement From Features of Time- and Value-Continuous, Dimensional Emotions, FRONTIERS IN COMPUTER SCIENCE, Vol: 4
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- Citations: 3
Mallol-Ragolta A, Semertzidou A, Pateraki M, et al., 2022, Outer Product-Based Fusion of Smartwatch Sensor Data for Human Activity Recognition, FRONTIERS IN COMPUTER SCIENCE, Vol: 4
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- Citations: 3
Amiriparian S, Huebner T, Karas V, et al., 2022, DeepSpectrumLite: A Power-Efficient Transfer Learning Framework for Embedded Speech and Audio Processing From Decentralized Data, FRONTIERS IN ARTIFICIAL INTELLIGENCE, Vol: 5
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- Citations: 3
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
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