TR2021-144
Convolutive Prediction for Monaural Speech Dereverberation and Noisy-Reverberant Speaker Separation
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- , "Convolutive Prediction for Monaural Speech Dereverberation and Noisy-Reverberant Speaker Separation", IEEE/ACM Transactions on Audio, Speech, and Language Processing, DOI: 10.1109/TASLP.2021.3129363, Vol. 29, pp. 3476-3490, December 2021.BibTeX TR2021-144 PDF
- @article{Wang2021dec,
- author = {Wang, Zhong-Qiu and Wichern, Gordon and Le Roux, Jonathan},
- title = {Convolutive Prediction for Monaural Speech Dereverberation and Noisy-Reverberant Speaker Separation},
- journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
- year = 2021,
- volume = 29,
- pages = {3476--3490},
- month = dec,
- doi = {10.1109/TASLP.2021.3129363},
- url = {https://www.merl.com/publications/TR2021-144}
- }
- , "Convolutive Prediction for Monaural Speech Dereverberation and Noisy-Reverberant Speaker Separation", IEEE/ACM Transactions on Audio, Speech, and Language Processing, DOI: 10.1109/TASLP.2021.3129363, Vol. 29, pp. 3476-3490, December 2021.
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Abstract:
We propose to exploit the linear-filter structure of reverberation within a supervised deep learning based monaural speech dereverberation framework. The key idea is to first estimate the direct-path signal of the target speaker using a DNN and then identify signals that are decayed and delayed copies of the estimated direct-path signal, as these can be reliably considered as reverberation. We then modify the proposed algorithm for speaker separation in reverberant and noisy-reverberant conditions. State-of-the-art speech dereverberation and speaker separation results are obtained on the REVERB, SMS-WSJ, and WHAMR! datasets.