The DOI matches its Crossref record; 35 of the 35 references it cites were found in a registry or an index.
Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation
Open accessAbstract
Monaural source separation is important for many real world applications. It is challenging because, with only a single channel of information available, without any constraints, an infinite number of solutions are possible. In this paper, we explore joint optimization of masking functions and deep recurrent neural networks for monaural source separation tasks, including speech separation, singing voice separation, and speech denoising. The joint optimization of the deep recurrent neural networks with an extra masking layer enforces a reconstruction constraint. Moreover, we explore a discriminative criterion for training neural networks to further enhance the separation performance. We evaluate the proposed system on the TSP, MIR-1K, and TIMIT datasets for speech separation, singing voice separation, and speech denoising tasks, respectively. Our approaches achieve 2.30-4.98 dB SDR gain compared to NMF models in the speech separation task, 2.30-2.48 dB GNSDR gain and 4.32-5.42 dB GSIR gain compared to existing models in the singing voice separation task, and outperform NMF and DNN baselines in the speech denoising task.
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Supervised Speech Separation Based on Deep Learning: An Overview
Wang, D., & Chen, J. (2018) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2018.2842159 · Cited by 1,524
Deep Learning for Audio Signal Processing
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Kolbaek, M., Yu, D., Tan, Z.-H., & Jensen, J. (2017) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2017.2726762 · Cited by 820
Audio-Visual Scene Analysis with Self-Supervised Multisensory Features
Owens, A., & Efros, A. A. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01231-1_39 · Cited by 704
TaSNet: Time-Domain Audio Separation Network for Real-Time, Single-Channel Speech Separation
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Metadata
- Authors
- Huang, P.-S., Kim, M., Hasegawa-Johnson, M., & Smaragdis, P.
- Venue
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
- Published
- 2015-12
- Volume
- 23
- Issue
- 12
- Pages
- 2136-2147
- ISSN
- 2329-9290 (ISSN-L)
- 2329-9304
- OpenAlex
- W1790748249
- Cited by
- 461
- References
- 35
- FWCI
- 66.42
- Type
- journal article
- Language
- en
- Open access
- green
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