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Deep learning for monaural speech separation
Abstract
Monaural source separation is useful for many real-world applications though it is a challenging problem. In this paper, we study deep learning for monaural speech separation. We propose the joint optimization of the deep learning models (deep neural networks and recurrent neural networks) with an extra masking layer, which enforces a reconstruction constraint. Moreover, we explore a discriminative training criterion for the neural networks to further enhance the separation performance. We evaluate our approaches using the TIMIT speech corpus for a monaural speech separation task. Our proposed models achieve about 3.8∼4.9 dB SIR gain compared to NMF models, while maintaining better SDRs and SARs.
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Cited by
Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures
Hershey, J. R., Roux, J. L., & Weninger, F. (2014) · arXiv · doi:10.48550/arxiv.1409.2574 · Cited by 412
Tan, K., & Wang, D. (2020) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2019.2955276 · Cited by 348
Distilled Binary Neural Network for Monaural Speech Separation
Chen, X., Liu, G., Shi, J., Xu, J., & Xu, B. (2018) · 2018 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489456 · Cited by 23
Metadata
- Authors
- Huang, P.-S., Kim, M., Hasegawa-Johnson, M., & Smaragdis, P.
- Venue
- 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2014-05
- Pages
- 1562-1566
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2031647436
- Cited by
- 423
- References
- 0
- FWCI
- 34.40
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Deep learning for monaural speech separation. https://app.cochat.ai/papers/10.1109/icassp.2014.6853860
- The paper
- Huang, P.-S., Kim, M., Hasegawa-Johnson, M., & Smaragdis, P. (2014). Deep learning for monaural speech separation. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2014.6853860
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