Nicholas Cummins
Nicholas Cummins
Bestätigte E-Mail-Adresse bei kcl.ac.uk
Titel
Zitiert von
Zitiert von
Jahr
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
4462015
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
2022017
Snore sound classification using image-based deep spectrum features
S Amiriparian, M Gerczuk, S Ottl, N Cummins, M Freitag, S Pugachevskiy, ...
1792017
An investigation of depressed speech detection: Features and normalization
N Cummins, J Epps, M Breakspear, R Goecke
Twelfth Annual Conference of the International Speech Communication Association, 2011
1382011
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
1042013
audeep: Unsupervised learning of representations from audio with deep recurrent neural networks
M Freitag, S Amiriparian, S Pugachevskiy, N Cummins, B Schuller
The Journal of Machine Learning Research 18 (1), 6340-6344, 2017
1032017
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
992017
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
952018
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
842019
Sequence to sequence autoencoders for unsupervised representation learning from audio
S Amiriparian, M Freitag, N Cummins, B Schuller
Proc. DCASE, 17-21, 2017
822017
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
672015
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
652015
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
622018
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
562020
Modeling spectral variability for the classification of depressed speech.
N Cummins, J Epps, V Sethu, M Breakspear, R Goecke
Interspeech, 857-861, 2013
522013
Advanced data exploitation in speech analysis: An overview
Z Zhang, N Cummins, B Schuller
IEEE Signal Processing Magazine 34 (4), 107-129, 2017
512017
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
482019
Spectro-temporal analysis of speech affected by depression and psychomotor retardation
N Cummins, J Epps, E Ambikairajah
2013 IEEE International Conference on Acoustics, Speech and Signal …, 2013
472013
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
442019
Variability compensation in small data: Oversampled extraction of i-vectors for the classification of depressed speech
N Cummins, J Epps, V Sethu, J Krajewski
2014 IEEE International Conference on Acoustics, Speech and Signal …, 2014
432014
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