Speech Enhancement Algorithm Based on Robust Principal Component Analysis with Whitened Spectrogram Rearrangement in Colored Noise

Yongjiang LUO; Tengfei YANG; Dong ZHAO

doi:10.11999/JEIT200594

Volume 43 Issue 12

Dec. 2021

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Yongjiang LUO, Tengfei YANG, Dong ZHAO. Speech Enhancement Algorithm Based on Robust Principal Component Analysis with Whitened Spectrogram Rearrangement in Colored Noise[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3671-3679. doi: 10.11999/JEIT200594

Citation:

Yongjiang LUO, Tengfei YANG, Dong ZHAO. Speech Enhancement Algorithm Based on Robust Principal Component Analysis with Whitened Spectrogram Rearrangement in Colored Noise[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3671-3679. doi: 10.11999/JEIT200594

Citation:

Yongjiang LUO, Tengfei YANG, Dong ZHAO. Speech Enhancement Algorithm Based on Robust Principal Component Analysis with Whitened Spectrogram Rearrangement in Colored Noise[J]. Journal of Electronics & Information Technology, 2021, 43(12): 3671-3679. doi: 10.11999/JEIT200594

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Speech Enhancement Algorithm Based on Robust Principal Component Analysis with Whitened Spectrogram Rearrangement in Colored Noise

doi: 10.11999/JEIT200594

School of Electronic Engineering, Xidian University, Xi’an 710071, China

Received Date: 2020-07-20
Rev Recd Date: 2021-03-25

Available Online: 2021-06-03

Publish Date: 2021-12-21

Abstract

Abstract

The Robust Principal Component Analysis (RPCA) based speech enhancement algorithm plays an important role for single channel speech processing in white Gaussian noise environment, but it has a poor processing effect on low-rank speech components and can not well suppress color noise. In view of this problem, an improved speech algorithm based on Whitening Spectrum Rearrangement RPCA (WSRRPCA) is proposed in this paper, which by optimizing the noise whitening model, color noise speech enhancement is converted into white noise speech signal processing, and spectrum rearrangement is used to improve RPCA speech enhancement processing algorithm to obtain an overall improvement in speech signal processing performance in a colored noise environment. Simulation experiments show that this algorithm can better achieve speech enhancement in a colored noise environment, and has better noise suppression and speech quality improvement capabilities than other algorithms.
- Speech enhancement,
- Robust Principal Component Analysis (RPCA),
- Colored noise,
- Noise whitening,
- Spectrogram rearrangement

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References(26)

References

[1]	LOIZOU P C. Speech Enhancement: Theory and Practice[M]. 2nd ed. London: CRC Press, 2013: 1–2.
[2]	BOLL S. Suppression of acoustic noise in speech using spectral subtraction[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1979, 27(2): 113–120. doi: 10.1109/tassp.1979.1163209
[3]	EPHRAIM Y and MALAH D. Speech enhancement using a minimum mean-square error log-spectral amplitude estimator[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1985, 33(2): 443–445. doi: 10.1109/TASSP.1985.1164550
[4]	SCALART P and FILHO J V. Speech enhancement based on a priori signal to noise estimation[C]. International Conference on Acoustics, Speech, and Signal Processing, Atlanta, 1996: 629–632. doi: 10.1109/ICASSP.1996.543199.
[5]	EPHRAIM Y and VAN TREES G L. A signal subspace approach for speech enhancement[J]. IEEE Transactions on Speech and Audio Processing, 1995, 3(4): 251–266. doi: 10.1109/89.397090
[6]	YI Hu and LOIZOU P C. A generalized subspace approach for enhancing speech corrupted by colored noise[J]. IEEE Transactions on Speech and Audio Processing, 2003, 11(4): 334–341. doi: 10.1109/TSA.2003.814458
[7]	SUN Chengli, ZHANG Qin, WANG Jian, et al. Noise reduction based on robust principal component analysis[J]. Journal of Computational Information Systems, 2014, 10(10): 4403–4410. doi: 10.12733/jcis10408
[8]	HUANG Jianjun, ZHANG Xiongwei, ZHANG Yafei, et al. Speech denoising via low-rank and sparse matrix decomposition[J]. ETRI Journal, 2014, 36(1): 167–170. doi: 10.4218/etrij.14.0213.0033
[9]	MAVADDATY S, AHADI S M, and SEYEDIN S. A novel speech enhancement method by learnable sparse and low-rank decomposition and domain adaptation[J]. Speech Communication, 2016, 76: 42–60. doi: 10.1016/j.specom.2015.11.003
[10]	SUN Pengfei and QIN Jun. Low-rank and sparsity analysis applied to speech enhancement via online estimated dictionary[J]. IEEE Signal Processing Letters, 2016, 23(12): 1862–1866. doi: 10.1109/lsp.2016.2627029
[11]	LUO Yongjiang and MAO Yu. Single-channel speech enhancement based on multi-band spectrogram-rearranged RPCA[J]. Electronics Letters, 2019, 55(7): 415–417. doi: 10.1049/el.2018.8131
[12]	CANDÈS E J, LI Xiaodong, MA Yi, et al. Robust principal component analysis?[J]. Journal of the ACM, 2011, 58(3): 11. doi: 10.1145/1970392.1970395
[13]	NAZIH M, MINAOUI K, and COMON P. Using the proximal gradient and the accelerated proximal gradient as a canonical polyadic tensor decomposition algorithms in difficult situations[J]. Signal Processing, 2020, 171: 107472. doi: 10.1016/j.sigpro.2020.107472
[14]	FENG Peihua, LING B W K, LEI Ruisheng, et al. Singular spectral analysis-based denoising without computing singular values via augmented Lagrange multiplier algorithm[J]. IET Signal Processing, 2019, 13(2): 149–156. doi: 10.1049/iet-spr.2018.5086
[15]	LEI Yunwen and ZHOU Dingxuan. Analysis of singular value thresholding algorithm for matrix completion[J]. Journal of Fourier Analysis and Applications, 2019, 25(6): 2957–2972. doi: 10.1007/s00041-019-09688-8
[16]	JARAMILLO A E, NIELSEN J K, CHRISTENSEN M G, et al. A study on how pre-whitening influences fundamental frequency estimation[C]. International Conference on Acoustics, Speech and Signal Processing, Brighton, England, 2019: 6495–6499. doi: 10.1109/ICASSP.2019.8683653.
[17]	VASEGHI S V. Advanced Digital Signal Processing and Noise Reduction[M]. 4th ed. Hoboken: John Wiley & Sons, 2008: 229–230.
[18]	SMITH III J O. Spectral Audio Signal Processing[M]. W3K Publishing, USA, 2011: 298–301.
[19]	张明, 刘祥楼, 姜峥嵘. 基于LPC的语音信号预测仿真分析[J]. 光学仪器, 2015, 37(1): 71–74. doi: 10.3969/j.issn.1005-5630.2015.01.015 ZHANG Ming, LIU Xianglou, and JIANG Zhengrong. Simulation analysis of speech signal prediction based on LPC[J]. Optical Instruments, 2015, 37(1): 71–74. doi: 10.3969/j.issn.1005-5630.2015.01.015
[20]	KAPRALOV M. Sparse Fourier transform in any constant dimension with nearly-optimal sample complexity in sublinear time[C]. The Forty-eighth Annual ACM Symposium on Theory of Computing, Virtual Event, Italy, 2016: 264–277. doi: 10.1145/2897518.2897650.
[21]	WANG D L and LIM J S. The unimportance of phase in speech enhancement[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1982, 30(4): 679–681. doi: 10.1109/TASSP.1982.1163920
[22]	VINCENT E, GRIBONVAL R, and FEVOTTE C. Performance measurement in blind audio source separation[J]. IEEE Transactions on Audio, Speech, and Language Processing, 2006, 14(4): 1462–1469. doi: 10.1109/TSA.2005.858005
[23]	RAM R and MOHANTY M N. Use of radial basis function network with discrete wavelet transform for speech enhancement[J]. International Journal of Computational Vision and Robotics, 2019, 9(2): 207–223. doi: 10.1504/IJCVR.2019.10019996
[24]	SUN Chengli, ZHU Qi, and WAN Minghua. A novel speech enhancement method based on constrained low-rank and sparse matrix decomposition[J]. Speech Communication, 2014, 60: 44–55. doi: 10.1016/j.specom.2014.03.002
[25]	LU Yang and LOIZOU P C. A geometric approach to spectral subtraction[J]. Speech Communication, 2008, 50(6): 453–466. doi: 10.1016/j.specom.2008.01.003
[26]	COHEN I. Optimal speech enhancement under signal presence uncertainty using log-spectral amplitude estimator[J]. IEEE Signal Processing Letters, 2002, 9(4): 113–116. doi: 10.1109/97.1001645