Nicholas Cummins
Cited by
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A review of depression and suicide risk assessment using speech analysis
N Cummins, S Scherer, J Krajewski, S Schnieder, J Epps, TF Quatieri
Speech communication 71, 10-49, 2015
Avec 2017: Real-life depression, and affect recognition workshop and challenge
F Ringeval, B Schuller, M Valstar, J Gratch, R Cowie, S Scherer, S Mozgai, ...
Proceedings of the 7th annual workshop on audio/visual emotion challenge, 3-9, 2017
Snore sound classification using image-based deep spectrum features
S Amiriparian, M Gerczuk, S Ottl, N Cummins, M Freitag, S Pugachevskiy, ...
AVEC 2019 workshop and challenge: state-of-mind, detecting depression with AI, and cross-cultural affect recognition
F Ringeval, B Schuller, M Valstar, N Cummins, R Cowie, L Tavabi, ...
Proceedings of the 9th International on Audio/visual Emotion Challenge and …, 2019
An investigation of depressed speech detection: Features and normalization
N Cummins, J Epps, M Breakspear, R Goecke
INTERSPEECH 2011 12th Annual Conference of the International Speech …, 2011
Using smartphones and wearable devices to monitor behavioral changes during COVID-19
S Sun, AA Folarin, Y Ranjan, Z Rashid, P Conde, C Stewart, N Cummins, ...
Journal of medical Internet research 22 (9), e19992, 2020
AVEC 2018 workshop and challenge: Bipolar disorder and cross-cultural affect recognition
F Ringeval, B Schuller, M Valstar, R Cowie, H Kaya, M Schmitt, ...
Proceedings of the 2018 on audio/visual emotion challenge and workshop, 3-13, 2018
audeep: Unsupervised learning of representations from audio with deep recurrent neural networks
M Freitag, S Amiriparian, S Pugachevskiy, N Cummins
Journal of Machine Learning Research 18 (173), 1-5, 2018
Speech analysis for health: Current state-of-the-art and the increasing impact of deep learning
N Cummins, A Baird, BW Schuller
Methods 151, 41-54, 2018
An image-based deep spectrum feature representation for the recognition of emotional speech
N Cummins, S Amiriparian, G Hagerer, A Batliner, S Steidl, BW Schuller
Proceedings of the 25th ACM international conference on Multimedia, 478-484, 2017
Diagnosis of depression by behavioural signals: a multimodal approach
N Cummins, J Joshi, A Dhall, V Sethu, R Goecke, J Epps
Proceedings of the 3rd ACM international workshop on Audio/visual emotion …, 2013
Exploring deep spectrum representations via attention-based recurrent and convolutional neural networks for speech emotion recognition
Z Zhao, Z Bao, Y Zhao, Z Zhang, N Cummins, Z Ren, B Schuller
IEEE access 7, 97515-97525, 2019
Sequence to sequence autoencoders for unsupervised representation learning from audio
S Amiriparian, M Freitag, N Cummins, B Schuller
DCASE, 17-21, 2017
Analysis of acoustic space variability in speech affected by depression
N Cummins, V Sethu, J Epps, S Schnieder, J Krajewski
Speech Communication 75, 27-49, 2015
An investigation of annotation delay compensation and output-associative fusion for multimodal continuous emotion prediction
Z Huang, T Dang, N Cummins, B Stasak, P Le, V Sethu, J Epps
Proceedings of the 5th International Workshop on Audio/Visual Emotion …, 2015
Adversarial training in affective computing and sentiment analysis: Recent advances and perspectives
J Han, Z Zhang, N Cummins, B Schuller
IEEE Computational Intelligence Magazine 14 (2), 68-81, 2019
Combining a parallel 2D CNN with a self-attention Dilated Residual Network for CTC-based discrete speech emotion recognition
Z Zhao, Q Li, Z Zhang, N Cummins, H Wang, J Tao, BW Schuller
Neural Networks 141, 52-60, 2021
Emotional expression in psychiatric conditions: New technology for clinicians
K Grabowski, A Rynkiewicz, A Lassalle, S Baron‐Cohen, B Schuller, ...
Psychiatry and clinical neurosciences 73 (2), 50-62, 2019
Learning image-based representations for heart sound classification
Z Ren, N Cummins, V Pandit, J Han, K Qian, B Schuller
Proceedings of the 2018 international conference on digital health, 143-147, 2018
Automatic assessment of depression from speech via a hierarchical attention transfer network and attention autoencoders
Z Zhao, Z Bao, Z Zhang, J Deng, N Cummins, H Wang, J Tao, B Schuller
IEEE Journal of Selected Topics in Signal Processing 14 (2), 423-434, 2019
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