The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
TaSNet: Time-Domain Audio Separation Network for Real-Time, Single-Channel Speech Separation
Open accessAbstract
Robust speech processing in multi-talker environments requires effective speech separation. Recent deep learning systems have made significant progress toward solving this problem, yet it remains challenging particularly in real-time, short latency applications. Most methods attempt to construct a mask for each source in time-frequency representation of the mixture signal which is not necessarily an optimal representation for speech separation. In addition, time-frequency decomposition results in inherent problems such as phase/magnitude decoupling and long time window which is required to achieve sufficient frequency resolution. We propose Time-domain Audio Separation Network (TasNet) to overcome these limitations. We directly model the signal in the time-domain using an encoder-decoder framework and perform the source separation on nonnegative encoder outputs. This method removes the frequency decomposition step and reduces the separation problem to estimation of source masks on encoder outputs which is then synthesized by the decoder. Our system outperforms the current state-of-the-art causal and noncausal speech separation algorithms, reduces the computational cost of speech separation, and significantly reduces the minimum required latency of the output. This makes TasNet suitable for applications where low-power, real-time implementation is desirable such as in hearable and telecommunication devices.
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Cites
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Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946
Identity mappings in deep residual networks
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Identity Mappings in Deep Residual Networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · arXiv · doi:10.48550/arxiv.1603.05027 · Cited by 10,665
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“We apply curriculum training strategy [24] in a similar fashion with [3, 5].”
MethodWaveNet: a generative model for raw audio
Oord, A. V. D., Dieleman, S., Zen, H., Simonyan, K., Vinyals, O., Graves, A., Kalchbrenner, N., Senior, A., & Kavukcuoglu, K. (2016) · arXiv · doi:10.48550/arxiv.1609.03499 · Cited by 4,911
Performance measurement in blind audio source separation
Vincent, E., Gribonval, R., & Fevotte, C. (2006) · IEEE Transactions on Audio, Speech and Language Processing · doi:10.1109/tsa.2005.858005 · Cited by 2,889
Convex and Semi-Nonnegative Matrix Factorizations
Ding, C. H. Q., Li, T., & Jordan, M. I. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.277 · Cited by 1,317
SEGAN: Speech Enhancement Generative Adversarial Network
Pascual, S., Bonafonte, A., & Serrà, J. (2017) · Interspeech 2017 · doi:10.21437/interspeech.2017-1428 · Cited by 1,297
A guide to convolution arithmetic for deep learning
Dumoulin, V., & Visin, F. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.07285 · Cited by 1,172
Language Modeling with Gated Convolutional Networks
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Cited by
Conv-TasNet: Surpassing Ideal Time–Frequency Magnitude Masking for Speech Separation
Luo, Y., & Mesgarani, N. (2019) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2019.2915167 · Cited by 2,042
Self-citationWavesplit: End-to-End Speech Separation by Speaker Clustering
Zeghidour, N., & Grangier, D. (2021) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2021.3099291 · Cited by 257
Sparks of Large Audio Models: A Survey and Outlook
Latif, S., Shoukat, M., Shamshad, F., Usama, M., Ren, Y., Cuayáhuitl, H., Wang, W., Zhang, X., Togneri, R., Cambria, E., et al. (2023) · arXiv · doi:10.48550/arxiv.2308.12792 · Cited by 17
“These applications range from voice-activated assistants like Siri and Alexa [1, 2] to transcription services [3], and extend to telecommunication systems [4] and hearing aids [5].”
BackgroundArakawa, R., Nakagawa, M., & Yakura, H. (2025) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3743735 · Cited by 2
Metadata
- Authors
- Luo, Y., & Mesgarani, N.
- Venue
- 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- Publisher
- IEEE
- Published
- 2018-04
- Pages
- 696-700
- ISSN
- 1520-6149 (ISSN-L)
- OpenAlex
- W2962935966
- Cited by
- 686
- References
- 27
- FWCI
- 49.44
- Type
- conference paper
- Language
- en
- Open access
- green
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- Luo, Y., & Mesgarani, N. (2018). TaSNet: Time-Domain Audio Separation Network for Real-Time, Single-Channel Speech Separation. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). https://doi.org/10.1109/icassp.2018.8462116
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