Abstract
As speech enhancement concerned, blind speech deconvolution are widely used in speech dereverberation, automatic speech recognition and hearing aid designs. it is generally a single-input multiple-output (SIMO) system identification followed by a signal deconvolution. A novel approach, in which blind multichannel identifications of a SIMO system and its inverse system are performed concurrently with Kalman filter, is proposed in this paper. Speech deconvolution is the direct result of the algorithm. The state vector of the Kalman filter is composed of the multichannel impulse responses (MCIRs) of the SIMO system and its inverse system; the measurement equation of the Kalman filter is constructed with the cross relation (CR) among the observed signals and the input-output relation by feeding the deconvolved signal into the identified SIMO system. Comparing with traditional deconvolution methods, the proposed approach is more noise robust and computationally efficient.
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References
Ahmad, R., Khong, A. W. H., Hasan, M. K., & Naylor, P. A. (2015). The extended normalized multichannel FLMS algorithm for blind channel identification. In European signal processing conference (pp. 1–5). IEEE
Braun, S., & Habets, E. A. P. (2016). Online dereverberation for dynamic scenarios using a Kalman filter with an autoregressive model. IEEE Signal Processing Letters, 23(12), 1741–1754. https://doi.org/10.1109/LSP.2016.2616888.
Braun, S., & Habets, E. A. P. (2018). Linear Prediction based online dereverberation and noise reduction using alternating Kalman filters. IEEE Transactions on Audio, Speech, and Language Processing, 26(6), 1119–1129. https://doi.org/10.1109/TASLP.2018.2811247
Furuya, K., Sakauchi, S., & Kataoka, A. (2006). Speech dereverberation by combining MINT-based blind deconvolution and modified sepctral subtraction. In IEEE international conference on acoustics, speech and signal processing (pp. 813–816). https://doi.org/10.1109/ICASSP.2006.1660145
Godard, D. N. (1980). Self-recovering equalization and carrier tracking in two-dimensional data communication systems. IEEE Transactions on Communications, 28(11), 1867–1875. https://doi.org/10.1109/TCOM.1980.1094608
Hasan, M. K., Benesty, J., Naylor, P. A., & Ward, D. B. (2005, September). Improving robustness of blind adaptive multichannel identification algorithms using constraints. In Proceedings of the 13th European Signal Processing Conference IEEE.
Haykin, S. (1996). Adaptive filter theory (3rd Ed), (pp. 772–816, pp. 302–321). Prentice-Hall.
He, H., Chen, J., Benesty, J., & Tao, T. (2018). Noise robust frequency-domain adaptive blind multichannel identification with \(\ell _p\)-norm constraint. IEEE Transactions on Audio Speech and Language Processing, 26(9), 1608–1619. https://doi.org/10.1109/TASLP.2018.2835729
Huang, Y., & Benesty, J. (2002a). Adaptive blind channel identification: Multi-channel and least mean square and Newton algorithms. IEEE International Conference on Acoustics. https://doi.org/10.1109/ICASSP.2002.5744932
Huang, Y., & Benesty, J. (2002b). Adaptive multi-channel least mean square and Newton algorithms for blind channel identification. Signal Processing, 82, 1127–1138. https://doi.org/10.1016/S0165-1684(02)00247-5
Huang, Y., & Benesty, J. (2003). A class of frequency-domain adaptive approaches to blind multichannel identification. IEEE Transaction on Signal Processing, 51(1), 11–24. https://doi.org/10.1109/TSP.2002.806559.
Liao, L., & Khong, A. W. H. (2010). A noise robust multichannel algorithm for blind estimation of room impulse responses. In Proceedings of the 12th international workshop on acoustic echo and noise control (IWAENC).
Mayyala, Q., Abed-Meraim, K., & Zerguine, A. (2017). Structure-based subspace method for multichannel blind system identification. IEEE Signal Processing Letters, 24(8), 1183–1187. https://doi.org/10.1109/LSP.2017.2715418
Mei, T. (2017). Blind multichannel deconvolution with cascaded Kalman filters. DEStech Transactions on Computer Science and Engineering. https://doi.org/10.12783/dtcse/icmsie2017/18663
Mei, T. (2019). Blind multichannel identification based on Kalman filter and eigenvalue decomposition. International Journal of Speech Technology, 22(1), 1–11. https://doi.org/10.1007/s10772-018-09562-w
Miyoshi, M., & Kaneda, Y. (1988). Inverse filtering of room acoustics. IEEE Transactions on Acoustics Speech and Signal Processing, 36(2), 145–152. https://doi.org/10.1109/29.1509.
Moulines, E., Duhamel, P., Cardoso, J. F., & Mayrargue, S. (1995). Subspace methods for the blind identification of multichannel FIR filters. IEEE Transaction on Signal Processing, 43(2), 516–525. https://doi.org/10.1109/78.348133.
Schwartz, B., Gannot, S., & Habets, E. (2015). Online speech dereverberation using Kalman filter and EM algorithm. IEEE Transactions on Audio Speech and Language Processing, 23(2), 394–406. https://doi.org/10.1109/TASLP.2014.2372342
Song, S., Cheng, L., Luan, S., Yao, D., & Yan, Y. (2021). An integrated multi-channel approach for joint noise reduction and dereverberation. Applied Acoustics. https://doi.org/10.1016/j.apacoust.2020.107526.
Tong, L., Xu, G., & Kailath, T. (1994). Blind identification and equalization based on second-order statistics: A time domain approach. IEEE Transaction on Information Theory, 40(2), 340–349. https://doi.org/10.1109/18.312157.
Xu, G., Liu, H., Tong, L., & Kailath, T. (1995). A least-square approach to blind channel identification. IEEE Transactions on Signal Processing, 43(12), 2982–2992. https://doi.org/10.1109/78.476442
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Mei, T., He, G., Zhao, Y. et al. Blind identification of the inverse of SIMO system and deconvolution with Kalman filter. Int J Speech Technol 25, 975–986 (2022). https://doi.org/10.1007/s10772-022-10000-1
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DOI: https://doi.org/10.1007/s10772-022-10000-1