基于联合最大后验概率的语音增强算法
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摘要
针对传统谱减法存在的算法缺陷,提出一种基于联合最大后验概率的改进谱减法.传统谱减法通过获取带噪语音与噪声的幅度差值,并提取带噪语音的相位信息进行语音信号重建.该方法因为谱相减产生"音乐噪声",并因为相位估计不准确,导致低信噪比下信号增强效果不理想.为此,引入多频带谱减法和相位估计,通过划分频谱,分别在子频带进行谱减法,有效降低"音乐噪声"的影响;同时构建基于最大后验概率的相位估计器,联合信号幅度函数和相位函数,通过多次交替迭代得到相位估值.实验结果表明,相对于传统谱减法,在低信噪比下该算法有效提高增强语音的质量感知和可懂度.
In order to solve the defect of the traditional spectral subtraction algorithm, an improved spectral subtraction based on the joint maximum a posteriori probability is proposed. The traditional spectral subtraction was used to reconstruct the speech via obtaining difference of the amplitude between the noisy speech and noise and extracting the phase of the noisy speech. "Music noise" was produced by the method, and the effect of signal enhancement under low signal-to-noise ratio was not ideal because of inaccurate phase estimation. For this, the multiband spectral subtraction and phase estimation were introduced, and spectral subtraction was carried out in the subbands which were obtained by spectrum division. And it has worked well on reducing the influence of "music noise". Meanwhile, the phase estimator based on the maximum a posteriori probability was constructed which was obtained by combining the amplitude function and thephase function of the signal and alternate iteration. The experimental results show that, compared with the traditional spectral subtraction, the proposed algorithm has performed better in terms of the quality perception and intelligibility of the enhanced speech at low signal to noise ratio.
In order to solve the defect of the traditional spectral subtraction algorithm, an improved spectral subtraction based on the joint maximum a posteriori probability is proposed. The traditional spectral subtraction was used to reconstruct the speech via obtaining difference of the amplitude between the noisy speech and noise and extracting the phase of the noisy speech. "Music noise" was produced by the method, and the effect of signal enhancement under low signal-to-noise ratio was not ideal because of inaccurate phase estimation. For this, the multiband spectral subtraction and phase estimation were introduced, and spectral subtraction was carried out in the subbands which were obtained by spectrum division. And it has worked well on reducing the influence of "music noise". Meanwhile, the phase estimator based on the maximum a posteriori probability was constructed which was obtained by combining the amplitude function and thephase function of the signal and alternate iteration. The experimental results show that, compared with the traditional spectral subtraction, the proposed algorithm has performed better in terms of the quality perception and intelligibility of the enhanced speech at low signal to noise ratio.
引文
1韦高梧,冯祖勇.基于去噪技术的DSP语音识别系统设计.器传感器与微系统,2017, 36(1):108-111.
2 Hendriks RC, Gerkmann T, Jensen J. DFT-domain based single-microphone noise reduction for speech enhancement:A survey of the state of the art. Synthesis Lectures on Speech and Audio Processing,2013, 9(1):1-80.
3 Kleijn WB, Crespo JB, Hendriks RC, et al. Optimizing speech intelligibility in a noisy environment:A unified view.IEEE Signal Processing Magazine, 2015, 32(2):43-54.[doi:10.1109/MSP.2014.2365594]
4 Loizou PC. Speech enhancement:Theory and practice. Boca Raton, FL, USA:CRC Press, 2013.
5 Upadhyay N, Karmakar A. An improved multi-band spectral subtraction algorithm for enhancing speech in various noise environments. Procedia Engineering, 2013, 64:312-321.[doi:10.1016/j.proeng.2013.09.103]
6 Wojcicki K, Milacic M, Stark A, et al. Exploiting conjugate symmetry of the short-time Fourier spectrum for speech enhancement. IEEE Signal Processing Letters, 2008, 15:461-464.[doi:10.1109/LSP.2008.923579]
7 Mowlaee P, Kulmer J. Harmonic phase estimation in singlechannel speech enhancement using phase decomposition and SNR information. IEEE/ACM Transactions on Audio,Speech, and Language Processing, 2015, 23(9):1521-1532.[doi:10.1109/TASLP.2015.2439038]
8 Kulmer J, Mowlaee P. Phase estimation in single channel speech enhancement using phase decomposition. IEEE Signal Processing Letters, 2015, 22(5):598-602.[doi:10.1109/LSP.2014.2365040]
9 Mowlaee P, Kulmer J. Phase estimation in single-channel speech enhancement:Limits-potential. IEEE/ACM Transactions on Audio, Speech, and Language Processing,2015,23(8):1283-1294.[doi:10.1109/TASLP.2015.2430820]
10 Krawczyk-Becker M,Gerkmann T. An evaluation of the perceptual quality of phase-aware single-channel speech enhancement. The Journal of the Acoustical Society of America, 2016, 140(4):EL364-EL369.[doi:10.1121/1.4965288]
11 Krawczyk-Becker M, Gerkmann T. On MMSE-based estimation of amplitude and complex speech spectral coefficients under phase-uncertainty. IEEE/ACM Transactions on Audio, Speech, and Language Processing,2016,24(12):2251-2262.[doi:10.1109/TASLP.2016.2602549]
12 Kulmer J, Mowlaee P. Harmonic phase estimation in singlechannel speech enhancement using von mises distribution and prior SNR. Proceedings of 2015 IEEE International Conference on Acoustics, Speech and Signal Processing.Brisbane, QLD, Australia. 2015. 5063-5067.
13杜志然,周萍,景新幸,等.基于谱熵的耳语音增强研究.传感器与微系统,2012, 31(6):69-72.[doi:10.3969/j.issn.1000-9787.2012.06.021]
14吴进.语音信号处理实用教程.北京:人民邮电出版社,2015.287-298.
15 Mowlaee P, Stahl J, Kulmer J. Iterative joint MAP singlechannel speech enhancement given non-uniform phase prior.Speech Communication,2017,86:85-96.[doi:10:1016/j.specom.2016.11.008]
2 Hendriks RC, Gerkmann T, Jensen J. DFT-domain based single-microphone noise reduction for speech enhancement:A survey of the state of the art. Synthesis Lectures on Speech and Audio Processing,2013, 9(1):1-80.
3 Kleijn WB, Crespo JB, Hendriks RC, et al. Optimizing speech intelligibility in a noisy environment:A unified view.IEEE Signal Processing Magazine, 2015, 32(2):43-54.[doi:10.1109/MSP.2014.2365594]
4 Loizou PC. Speech enhancement:Theory and practice. Boca Raton, FL, USA:CRC Press, 2013.
5 Upadhyay N, Karmakar A. An improved multi-band spectral subtraction algorithm for enhancing speech in various noise environments. Procedia Engineering, 2013, 64:312-321.[doi:10.1016/j.proeng.2013.09.103]
6 Wojcicki K, Milacic M, Stark A, et al. Exploiting conjugate symmetry of the short-time Fourier spectrum for speech enhancement. IEEE Signal Processing Letters, 2008, 15:461-464.[doi:10.1109/LSP.2008.923579]
7 Mowlaee P, Kulmer J. Harmonic phase estimation in singlechannel speech enhancement using phase decomposition and SNR information. IEEE/ACM Transactions on Audio,Speech, and Language Processing, 2015, 23(9):1521-1532.[doi:10.1109/TASLP.2015.2439038]
8 Kulmer J, Mowlaee P. Phase estimation in single channel speech enhancement using phase decomposition. IEEE Signal Processing Letters, 2015, 22(5):598-602.[doi:10.1109/LSP.2014.2365040]
9 Mowlaee P, Kulmer J. Phase estimation in single-channel speech enhancement:Limits-potential. IEEE/ACM Transactions on Audio, Speech, and Language Processing,2015,23(8):1283-1294.[doi:10.1109/TASLP.2015.2430820]
10 Krawczyk-Becker M,Gerkmann T. An evaluation of the perceptual quality of phase-aware single-channel speech enhancement. The Journal of the Acoustical Society of America, 2016, 140(4):EL364-EL369.[doi:10.1121/1.4965288]
11 Krawczyk-Becker M, Gerkmann T. On MMSE-based estimation of amplitude and complex speech spectral coefficients under phase-uncertainty. IEEE/ACM Transactions on Audio, Speech, and Language Processing,2016,24(12):2251-2262.[doi:10.1109/TASLP.2016.2602549]
12 Kulmer J, Mowlaee P. Harmonic phase estimation in singlechannel speech enhancement using von mises distribution and prior SNR. Proceedings of 2015 IEEE International Conference on Acoustics, Speech and Signal Processing.Brisbane, QLD, Australia. 2015. 5063-5067.
13杜志然,周萍,景新幸,等.基于谱熵的耳语音增强研究.传感器与微系统,2012, 31(6):69-72.[doi:10.3969/j.issn.1000-9787.2012.06.021]
14吴进.语音信号处理实用教程.北京:人民邮电出版社,2015.287-298.
15 Mowlaee P, Stahl J, Kulmer J. Iterative joint MAP singlechannel speech enhancement given non-uniform phase prior.Speech Communication,2017,86:85-96.[doi:10:1016/j.specom.2016.11.008]