BP-CRN: A Lightweight Two-Stage Convolutional Recurrent Network for Multi-Channel Speech Enhancement
In our work, we propose a lightweight two-stage convolutional recurrent network (BP-CRN) for multichannel speech enhancement (mcse), which consists of beamforming and post-filtering. Drawing inspiration from traditional methods, we design two core modules for spatial filtering and post-filtering wit...
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| Published in | IEICE Transactions on Information and Systems Vol. E108.D; no. 2; pp. 161 - 164 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Tokyo
The Institute of Electronics, Information and Communication Engineers
01.02.2025
Japan Science and Technology Agency |
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| ISSN | 0916-8532 1745-1361 |
| DOI | 10.1587/transinf.2024EDL8042 |
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| Abstract | In our work, we propose a lightweight two-stage convolutional recurrent network (BP-CRN) for multichannel speech enhancement (mcse), which consists of beamforming and post-filtering. Drawing inspiration from traditional methods, we design two core modules for spatial filtering and post-filtering with compensation, named BM and PF, respectively. Both core modules employ a convolutional encoding-decoding structure and utilize complex frequency-time long short-term memory (CFT-LSTM) blocks in the middle. Furthermore, the inter-module mask module is introduced to estimate and convey implicit spatial information and assist the post-filtering module in refining spatial filtering and suppressing residual noise. Experimental results demonstrate that, our proposed method contains only 1.27M parameters and outperforms three other mcse methods in terms of PESQ and STOI metrics. |
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| AbstractList | In our work, we propose a lightweight two-stage convolutional recurrent network (BP-CRN) for multichannel speech enhancement (mcse), which consists of beamforming and post-filtering. Drawing inspiration from traditional methods, we design two core modules for spatial filtering and post-filtering with compensation, named BM and PF, respectively. Both core modules employ a convolutional encoding-decoding structure and utilize complex frequency-time long short-term memory (CFT-LSTM) blocks in the middle. Furthermore, the inter-module mask module is introduced to estimate and convey implicit spatial information and assist the post-filtering module in refining spatial filtering and suppressing residual noise. Experimental results demonstrate that, our proposed method contains only 1.27M parameters and outperforms three other mcse methods in terms of PESQ and STOI metrics. |
| ArticleNumber | 2024EDL8042 |
| Author | NI, Ye PANG, Cong CHENG, Jiaming ZHOU, Lin ZHAO, Li |
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| Cites_doi | 10.1109/ASRU51503.2021.9688198 10.1109/TASL.2011.2114881 10.1109/ASRU.2015.7404837 10.1109/ICASSP43922.2022.9746359 10.1023/A:1007515423169 10.1109/LSP.2013.2291240 10.1109/ICASSP.2001.941023 10.21437/Interspeech.2022-159 10.1109/ICASSP40776.2020.9054177 10.1109/ICASSP43922.2022.9746055 10.21437/Interspeech.2016-552 10.1109/LSP.2023.3244428 10.1109/ASRU.2015.7404793 10.21437/Interspeech.2020-2537 10.1109/ICASSP40776.2020.9053989 10.1109/ASRU51503.2021.9688326 10.1109/ICASSP49357.2023.10095770 |
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| References_xml | – reference: [10] R. Gu, S.-X. Zhang, M. Yu, and D. Yu, “3d spatial features for multi-channel target speech separation,” 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp.996-1002, IEEE, 2021. 10.1109/asru51503.2021.9688198 – reference: [14] J. Barker, R. Marxer, E. Vincent, and S. Watanabe, “The third ‘chime’ speech separation and recognition challenge: Dataset, task and baselines,” 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp.504-511, IEEE, 2015. 10.1109/asru.2015.7404837 – reference: [16] C.H. Taal, R.C. Hendriks, R. Heusdens, and J. Jensen, “An algorithm for intelligibility prediction of time-frequency weighted noisy speech,” IEEE Trans. Audio, Speech, Language Process., vol.19, no.7, pp.2125-2136, 2011. 10.1109/tasl.2011.2114881 – reference: [3] Y. Xu, J. Du, L.-R. Dai, and C.-H. Lee, “An experimental study on speech enhancement based on deep neural networks,” IEEE Signal Process. Lett., vol.21, no.1, pp.65-68, 2013. 10.1109/lsp.2013.2291240 – reference: [8] Y. Luo, Z. Chen, N. Mesgarani, and T. Yoshioka, “End-to-end microphone permutation and number invariant multi-channel speech separation,” ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.6394-6398, IEEE, 2020. 10.1109/icassp40776.2020.9054177 – reference: [15] A.W. Rix, J.G. Beerends, M.P. Hollier, and A.P. Hekstra, “Perceptual evaluation of speech quality (pesq)-a new method for speech quality assessment of telephone networks and codecs,” 2001 IEEE international conference on acoustics, speech, and signal processing. Proceedings (Cat. no.01CH37221), vol.2, pp.749-752, IEEE, 2001. 10.1109/icassp.2001.941023 – reference: [7] A. Li, G. Yu, C. Zheng, and X. Li, “Taylorbeamformer: Learning all-neural beamformer for multi-channel speech enhancement from taylor’s approximation theory,” Interspeech, pp.5413-5417, 2022. 10.21437/interspeech.2022-159 – reference: [5] H. Erdogan, J.R. Hershey, S. Watanabe, M.I. Mandel, and J.L. Roux, “Improved mvdr beamforming using single-channel mask prediction networks.,” Interspeech, pp.1981-1985, 2016. 10.21437/interspeech.2016-552 – reference: [4] Y. Hu, Y. Liu, S. Lv, M. Xing, S. Zhang, Y. Fu, J. Wu, B. Zhang, and L. Xie, “Dccrn: Deep complex convolution recurrent network for phase-aware speech enhancement,” arXiv preprint arXiv:2008. 00264, 2020. – reference: [1] J. Li, Y. Zhu, D. Luo, Y. Liu, G. Cui, and Z. Li, “The pcg-aiid system for l3das22 challenge: Mimo and miso convolutional recurrent network for multi channel speech enhancement and speech recognition,” ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.9211-9215, IEEE, 2022. 10.1109/icassp43922.2022.9746055 – reference: [6] X. Ji, L. Lu, F. Fang, J. Ma, L. Zhu, J. Li, D. Zhao, M. Liu, and F. Jiang, “An end-to-end far-field keyword spotting system with neural beamforming,” 2021 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU), pp.892-899, IEEE, 2021. 10.1109/asru51503.2021.9688326 – reference: [12] E. Bauer and R. Kohavi, “An empirical comparison of voting classification algorithms: Bagging, boosting, and variants,” Machine learning, vol.36, pp.105-139, 1999. 10.1023/a:1007515423169 – reference: [2] X. Xu, R. Gu, and Y. Zou, “Improving dual-microphone speech enhancement by learning cross-channel features with multi-head attention,” ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.6492-6496, IEEE, 2022. 10.1109/icassp43922.2022.9746359 – reference: [17] B. Tolooshams, R. Giri, A.H. Song, U. Isik, and A. Krishnaswamy, “Channel-attention dense u-net for multichannel speech enhancement,” ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.836-840, 2020. 10.1109/icassp40776.2020.9053989 – reference: [18] D. Lee and J.-W. Choi, “Deft-an: Dense frequency-time attentive network for multichannel speech enhancement,” IEEE Signal Process. Lett., vol.30, pp.155-159, 2023. 10.1109/lsp.2023.3244428 – reference: [9] J. Li, A. Mohamed, G. Zweig, and Y. Gong, “Lstm time and frequency recurrence for automatic speech recognition,” 2015 IEEE workshop on automatic speech recognition and understanding (ASRU), pp.187-191, IEEE, 2015. 10.1109/asru.2015.7404793 – reference: [11] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint arXiv:1412.3555, 2014. – reference: [13] H.S. Choi, J.H. Kim, J. Huh, A. Kim, J.W. Ha, and K. Lee, “Phase-aware speech enhancement with deep complex u-net,” arXiv preprint arXiv:1903.03107, 2019. – reference: [19] J. Cheng, C. Pang, R. Liang, J. Fan, and L. Zhao, “Dual-path dilated convolutional recurrent network with group attention for multi-channel speech enhancement,” ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.1-2, 2023. 10.1109/icassp49357.2023.10095770 – ident: 10 doi: 10.1109/ASRU51503.2021.9688198 – ident: 16 doi: 10.1109/TASL.2011.2114881 – ident: 14 doi: 10.1109/ASRU.2015.7404837 – ident: 2 doi: 10.1109/ICASSP43922.2022.9746359 – ident: 11 – ident: 12 doi: 10.1023/A:1007515423169 – ident: 3 doi: 10.1109/LSP.2013.2291240 – ident: 15 doi: 10.1109/ICASSP.2001.941023 – ident: 7 doi: 10.21437/Interspeech.2022-159 – ident: 13 – ident: 8 doi: 10.1109/ICASSP40776.2020.9054177 – ident: 1 doi: 10.1109/ICASSP43922.2022.9746055 – ident: 5 doi: 10.21437/Interspeech.2016-552 – ident: 18 doi: 10.1109/LSP.2023.3244428 – ident: 9 doi: 10.1109/ASRU.2015.7404793 – ident: 4 doi: 10.21437/Interspeech.2020-2537 – ident: 17 doi: 10.1109/ICASSP40776.2020.9053989 – ident: 6 doi: 10.1109/ASRU51503.2021.9688326 – ident: 19 doi: 10.1109/ICASSP49357.2023.10095770 |
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| SubjectTerms | Beamforming complex network convolutional recurrent network Encoding-Decoding lightweight Modules multichannel speech enhancement neural beamforming Spatial data Spatial filtering Speech processing |
| Title | BP-CRN: A Lightweight Two-Stage Convolutional Recurrent Network for Multi-Channel Speech Enhancement |
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