Browse > Article
http://dx.doi.org/10.7776/ASK.2020.39.5.490

Improved speech enhancement of multi-channel Wiener filter using adjustment of principal subspace vector  

Kim, Gibak (School of Electrical Engineering, Soongsil University)
Publication Information
The Journal of the Acoustical Society of Korea / v.39, no.5, 2020 , pp. 490-496 More about this Journal
Abstract
We present a method to improve the performance of the multi-channel Wiener filter in noisy environment. To build subspace-based multi-channel Wiener filter, in the case of single target source, the target speech component can be effectively estimated in the principal subspace of speech correlation matrix. The speech correlation matrix can be estimated by subtracting noise correlation matrix from signal correlation matrix based on the assumption that the cross-correlation between speech and interfering noise is negligible compared with speech correlation. However, this assumption is not valid in the presence of strong interfering noise and significant error can be induced in the principal subspace accordingly. In this paper, we propose to adjust the principal subspace vector using speech presence probability and the steering vector for the desired speech source. The multi-channel speech presence probability is derived in the principal subspace and applied to adjust the principal subspace vector. Simulation results show that the proposed method improves the performance of multi-channel Wiener filter in noisy environment.
Keywords
Noise reduction; Multi-channel Wiener filter; Speech presence probability; Subspace decomposition;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 K. Ngo, M. Moonen, S. H. Jensen, and J. Wouters, "A flexible speech distortion weighted multi-channel Wiener filter for noise reduction in hearing aids," ICASSP. 2528-2531 (2011).
2 T. C. Lawin-Ore, S. Stenzel, J. Freudenberger, and S. Doclo, "Generalized multichannel wiener filter for spatially distributed microphones," Speech Communication;. 11. ITG Symposium, 1-4 (2014).
3 R. Serizel, M. Moonen, B. Van Dijk, and J. Wouters, "Low-rank approximation based multichannel Wiener filter algorithms for noise reduction with application in cochlear implants," IEEE/ACM Trans. Audio, Speech, and Lang. Process. 22, 785-798 (2014).   DOI
4 G. Kim, "Interference suppression using principal subspace modification in multichannel Wiener filter and its application to speech recognition," ETRI J. 32, 921-931 (2010).   DOI
5 K. Ngo, A. Spriet, M. Moonen, J. Wouter, and S. H. Jensen, "Incorporating the conditional speech presence probability in multi-channel Wiener filter based noise reduction in hearing aids," Eurasip J. Advances in Signal Processing, 2009, 1-11 (2009).
6 Z. Wang, E. Vincent, R. Serizel, and Y. Yan, "Rank-1 constrained multichannel Wiener filter for speech recognition in noisy environments," Computer Speech & Language, 49, 37-51 (2018).   DOI
7 S. Bagheri and D. Giacobello, "Exploiting multichaannel speech presence probability in parametric multi-channel Wiener filter," Interspeech, 101-105 (2019).
8 M. Souden, J. Chen, J. Benesty, and S. Affes, "Gaussian model-based multichannel speech presence probability," IEEE Trans. Audio, Speech, Lang. Process. 18, 1072-1077 (2010).   DOI
9 S. Jeong and Y. Kim, "An optimally-modified multichannel Wiener filter using speech presence probability" (in Korean), Smart Media J. 7, 9-15 (2018).
10 Y. G. Jin, J. W. Shin, and N. S. Kim, "Decisiondirected speech power spectral density matrix estimation for multichannel speech enhancement," J. Acoust. Soc. Am. 141, EL234 (2017).   DOI
11 G. H. Golub and C. F. Van Loan, Matrix Computations, 3rd ed. (Johns Hopkins University Press, Baltimore, 1996), Chap. 8.
12 H. Van Trees, Optimum Array Processing: Part IV of Detection, Estimation and Modulation Theory (Wiley, Hoboken, 2002), Chap. 2.
13 S. Nakamura, K. Hiyane, F. Asano, T. Nishiura, and T. Yamada "Acoustical sound database in real environments for sound scene understanding and hands-free speech recognition," Proc. the 2nd Int. Conf. LREC. 965-968 (2000).