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This paper proposes MP-SENet, a novel Speech Enhancement Network which directly denoises Magnitude and Phase spectra in parallel.
K. Paliwal, K. Wójcicki, and B. Shannon, “The importance of phase in speech enhancement,” Speech Communication , vol. 53, no. 4, pp. 465–494, 2011
2011
Earlier work this paper cites.
C. Veaux, J. Yamagishi, and S. King, “The voice bank corpus: Design, collection and data analysis of a large regional accent speech database,” in Proc. O-COCOSDA/CASLRE , 2013, pp. 1–4
2013
Earlier work this paper cites.
J. Thiemann, N. Ito, and E. Vincent, “The diverse environments multi-channel acoustic noise database (DEMAND): A database of multichannel environmental noise recordings,” in Proc. ICA , vol. 19, no. 1, 2013, p. 035081
2013
Earlier work this paper cites.
Y. Xu, J. Du, L.-R. Dai, and C.-H. Lee, “A regression approach to speech enhancement based on deep neural networks,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 23, no. 1, pp. 7–19, 2014
2014
Earlier work this paper cites.
D. S. Williamson, Y. Wang, and D. Wang, “Complex ratio masking for monaural speech separation,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 24, no. 3, pp. 483–492, 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Delving deep into rectifiers: Surpassing human-level performance on imagenet classification,” in Proc. ICCV , 2015, pp. 1026–1034
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
C. Valentini-Botinhao, X. Wang, S. Takaki, and J. Yamagishi, “Investigating RNN-based speech enhancement methods for noise-robust text-to-speech.” in Proc. SSW , 2016, pp. 146–152
2016
Earlier work this paper cites.
S. Pascual, A. Bonafonte, and J. Serrà, “SEGAN: Speech enhancement generative adversarial network,” in Proc. Interspeech , 2017, pp. 3642–3646
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
C. Valentini-Botinhao and J. Yamagishi, “Speech enhancement of noisy and reverberant speech for text-to-speech,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 26, no. 8, pp. 1420–1433, 2018
2018
Cited alongside, same era.
A. Pandey and D. Wang, “TCNN: Temporal convolutional neural network for real-time speech enhancement in the time domain,” in Proc. ICASSP , 2019, pp. 6875–6879
2019
Cited alongside, same era.
Y. Ai, J.-X. Zhang, L. Chen, and Z.-H. Ling, “DNN-based spectral enhancement for neural waveform generators with low-bit quantization,” in Proc. ICASSP , 2019, pp. 7025–7029
2019
Cited alongside, same era.
K. Tan and D. Wang, “Learning complex spectral mapping with gated convolutional recurrent networks for monaural speech enhancement,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 28, pp. 380–390, 2019
2019
Cited alongside, same era.
Z.-Q. Wang, G. Wichern, and J. Le Roux, “On the compensation between magnitude and phase in speech separation,” IEEE Signal Processing Letters , vol. 28, pp. 2018–2022, 2021
2021
Later among the works it cites.
S.-W. Fu, C. Yu, T.-A. Hsieh, P. Plantinga, M. Ravanelli, X. Lu, and Y. Tsao, “MetricGAN+: An improved version of MetricGAN for speech enhancement,” in Proc. Interspeech , 2021, pp. 201–205
2021
Later among the works it cites.
Z. Kong, W. Ping, A. Dantrey, and B. Catanzaro, “Speech denoising in the waveform domain with self-attention,” in Proc. ICASSP , 2022, pp. 7867–7871
2022
Later among the works it cites.
F. Dang, H. Chen, and P. Zhang, “DPT-FSNet: Dual-path transformer based full-band and sub-band fusion network for speech enhancement,” in Proc. ICASSP , 2022, pp. 6857–6861
2022
Later among the works it cites.
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A. Défossez, G. Synnaeve, and Y. Adi, “Real time speech enhancement in the waveform domain,” in Proc. Interspeech , 2020, pp. 3291–3295
2020
Cited alongside, same era.
J. Kim, M. El-Khamy, and J. Lee, “T-GSA: Transformer with Gaussian-weighted self-attention for speech enhancement,” in Proc. ICASSP , 2020, pp. 6649–6653
2020
Cited alongside, same era.
D. Yin, C. Luo, Z. Xiong, and W. Zeng, “PHASEN: A phase-and-harmonics-aware speech enhancement network,” in Proc. AAAI , vol. 34, no. 05, 2020, pp. 9458–9465
2020
Cited alongside, same era.
A. Pandey and D. Wang, “Densely connected neural network with dilated convolutions for real-time speech enhancement in the time domain,” in Proc. ICASSP , 2020, pp. 6629–6633
2020
Cited alongside, same era.
E. Kim and H. Seo, “SE-Conformer: Time-domain speech enhancement using conformer.” in Proc. Interspeech , 2021, pp. 2736–2740
2021
Cited alongside, same era.
2022
Later among the works it cites.
G. Yu, A. Li, C. Zheng, Y. Guo, Y. Wang, and H. Wang, “Dual-branch attention-in-attention transformer for single-channel speech enhancement,” in Proc. ICASSP , 2022, pp. 7847–7851
2022
Later among the works it cites.
R. Cao, S. Abdulatif, and B. Yang, “CMGAN: Conformer-based Metric GAN for Speech Enhancement,” in Proc. Interspeech , 2022, pp. 936–940
2022
Later among the works it cites.
Y. Ai and Z.-H. Ling, “Neural speech phase prediction based on parallel estimation architecture and anti-wrapping losses,” in Proc. ICASSP , 2023
2023
Closest in time.
S.-W. Fu, C.-F. Liao, Y. Tsao, and S.-D. Lin, “MetricGAN: Generative adversarial networks based black-box metric scores optimization for speech enhancement,” in Proc. ICML , 2019, pp. 2031–2041
2041
Closest in time.