The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
Learning Spectral Mapping for Speech Dereverberation and Denoising
Abstract
In real-world environments, human speech is usually distorted by both reverberation and background noise, which have negative effects on speech intelligibility and speech quality. They also cause performance degradation in many speech technology applications, such as automatic speech recognition. Therefore, the dereverberation and denoising problems must be dealt with in daily listening environments. In this paper, we propose to perform speech dereverberation using supervised learning, and the supervised approach is then extended to address both dereverberation and denoising. Deep neural networks are trained to directly learn a spectral mapping from the magnitude spectrogram of corrupted speech to that of clean speech. The proposed approach substantially attenuates the distortion caused by reverberation, as well as background noise, and is conceptually simple. Systematic experiments show that the proposed approach leads to significant improvements of predicted speech intelligibility and quality, as well as automatic speech recognition in reverberant noisy conditions. Comparisons show that our approach substantially outperforms related methods.
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
(2010) · Cited by 8,261
Kaldi Speech Recognition Toolkit
Povey, D. (2024) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · doi:10.57702/jb3fvbn9 · Cited by 4,609
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051
Rix, A. W., Beerends, J. G., Hollier, M. P., & Hekstra, A. P. (2002) · 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221) · doi:10.1109/icassp.2001.941023 · Cited by 3,149
An Algorithm for Intelligibility Prediction of Time–Frequency Weighted Noisy Speech
Taal, C. H., Hendriks, R. C., Heusdens, R., & Jensen, J. (2011) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2011.2114881 · Cited by 2,254
Signal estimation from modified short-time Fourier transform
Griffin, D., & Lim, J. (1984) · IEEE Transactions on Acoustics, Speech, and Signal Processing · doi:10.1109/tassp.1984.1164317 · Cited by 2,095
IEEE Recommended Practice for Speech Quality Measurements
(1969) · IEEE Transactions on Audio and Electroacoustics · doi:10.1109/tau.1969.1162058 · Cited by 1,232
An Experimental Study on Speech Enhancement Based on Deep Neural Networks
Xu, Y., Du, J., Dai, L.-R., & Lee, C.-H. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2013.2291240 · Cited by 924
Cited by
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
Self-citationMachine learning in acoustics: Theory and applications
Bianco, M. J., Gerstoft, P., Traer, J., Ozanich, E., Roch, M. A., Gannot, S., & Deledalle, C.-A. (2019) · The Journal of the Acoustical Society of America · doi:10.1121/1.5133944 · Cited by 609
A New Framework for CNN-Based Speech Enhancement in the Time Domain
Pandey, A., & Wang, D. (2019) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2019.2913512 · Cited by 299
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Metadata
- Authors
- Han, K., Wang, Y., Wang, D., Woods, W. S., Merks, I., & Zhang, T.
- Venue
- IEEE/ACM Transactions on Audio, Speech, and Language Processing
- Published
- 2015-06
- Volume
- 23
- Issue
- 6
- Pages
- 982-992
- ISSN
- 2329-9290 (ISSN-L)
- 2329-9304
- OpenAlex
- W2006129368
- Cited by
- 260
- References
- 23
- FWCI
- 34.44
- Type
- journal article
- Language
- en
- Open access
- closed
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