The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Emotion recognition using deep learning approach from audio–visual emotional big data
Abstract
Abstract This paper proposes an emotion recognition system using a deep learning approach from emotional Big Data. The Big Data comprises of speech and video. In the proposed system, a speech signal is first processed in the frequency domain to obtain a Mel-spectrogram, which can be treated as an image. Then this Mel-spectrogram is fed to a convolutional neural network (CNN). For video signals, some representative frames from a video segment are extracted and fed to the CNN. The outputs of the two CNNs are fused using two consecutive extreme learning machines (ELMs). The output of the fusion is given to a support vector machine (SVM) for final classification of the emotions. The proposed system is evaluated using two audio–visual emotional databases, one of which is Big Data. Experimental results confirm the effectiveness of the proposed system involving the CNNs and the ELMs.
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LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Learning Spatiotemporal Features with 3D Convolutional Networks
Tran, D., Bourdev, L., Fergus, R., Torresani, L., & Paluri, M. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.510 · Cited by 9,593
A database of German emotional speech
Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W. F., & Weiss, B. (2005) · Interspeech 2005 · doi:10.21437/interspeech.2005-446 · Cited by 2,365
Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning
Ng, H.-W., Nguyen, V. D., Vonikakis, V., & Winkler, S. (2015) · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction · doi:10.1145/2818346.2830593 · Cited by 657
The eNTERFACE’05 Audio-Visual Emotion Database
Martin, O., Kotsia, I., Macq, B., & Pitas, I. (2006) · 22nd International Conference on Data Engineering Workshops (ICDEW'06) · doi:10.1109/icdew.2006.145 · Cited by 615
Evaluating deep learning architectures for Speech Emotion Recognition
Fayek, H. M., Lech, M., & Cavedon, L. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.02.013 · Cited by 554
FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition
Ding, H., Zhou, S. K., & Chellappa, R. (2017) · 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017) · doi:10.1109/fg.2017.23 · Cited by 416
Deep learning for robust feature generation in audiovisual emotion recognition
Kim, Y., Lee, H., & Provost, E. M. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638346 · Cited by 401
Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition
Zhang, S., Zhang, S., Huang, T., Gao, W., & Tian, Q. (2018) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2719043 · Cited by 346
Emotion-aware connected healthcare big data towards 5G
Hossain, M. S., & Muhammad, G. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2772959 · Cited by 273
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Dhillon, A., & Verma, G. K. (2020) · Progress in Artificial Intelligence · doi:10.1007/s13748-019-00203-0 · Cited by 1,119
Emotion recognition using multi-modal data and machine learning techniques: A tutorial and review
Zhang, J., Yin, Z., Chen, P., & Nichele, S. (2020) · Information Fusion · doi:10.1016/j.inffus.2020.01.011 · Cited by 806
Artificial intelligence in the creative industries: a review
Anantrasirichai, N., & Bull, D. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-021-10039-7 · Cited by 793
Speech Emotion Recognition Using Deep Learning Techniques: A Review
Khalil, R. A., Jones, E., Babar, M. I., Jan, T., Zafar, M. H., & Alhussain, T. (2019) · IEEE Access · doi:10.1109/access.2019.2936124 · Cited by 702
Deep Learning for EEG motor imagery classification based on multi-layer CNNs feature fusion
Amin, S. U., Alsulaiman, M., Muhammad, G., Mekhtiche, M. A., & Shamim Hossain, M. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.06.027 · Cited by 523
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Metadata
- Authors
- Hossain, M. S., & Muhammad, G.
- Venue
- Information Fusion
- Publisher
- Elsevier BV
- Published
- 2019-09
- Volume
- 49
- Pages
- 69-78
- ISSN
- 1566-2535 (ISSN-L)
- OpenAlex
- W2918087949
- Cited by
- 453
- References
- 12
- FWCI
- 55.53
- Type
- journal article
- Language
- en
- Open access
- closed
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