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http://dx.doi.org/10.7776/ASK.2011.30.7.383

An Improved Speech Absence Probability Estimation based on Environmental Noise Classification  

Son, Young-Ho (인하대학교 전자공학부)
Park, Yun-Sik (인하대학교 전자공학부)
An, Hong-Sub (인하대학교 전자공학부)
Lee, Sang-Min (인하대학교 전자공학부)
Publication Information
The Journal of the Acoustical Society of Korea / v.30, no.7, 2011 , pp. 383-389 More about this Journal
Abstract
In this paper, we propose a improved speech absence probability estimation algorithm by applying environmental noise classification for speech enhancement. The previous speech absence probability required to seek a priori probability of speech absence was derived by applying microphone input signal and the noise signal based on the estimated value of a posteriori SNR threshold. In this paper, the proposed algorithm estimates the speech absence probability using noise classification algorithm which is based on Gaussian mixture model in order to apply the optimal parameter each noise types, unlike the conventional fixed threshold and smoothing parameter. Performance of the proposed enhancement algorithm is evaluated by ITU-T P.862 PESQ (perceptual evaluation of speech quality) and composite measure under various noise environments. It is verified that the proposed algorithm yields better results compared to the conventional speech absence probability estimation algorithm.
Keywords
Speech absence probability; Gaussian mixture model (GMM); Noise Classification;
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1 Y. Ephraim and D. Malah, "Speech enhancement using a minimum mean-square error short-time spectral amplitude estimator." IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-32, no. 6, pp. 1109-1121, 1984.
2 Y. Epharim and D. Malah, "Speech enhancement using a minimum mean-square error log-spectral amplitude estimator," IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-32, no. 2, pp. 443-445, 1985.
3 S. F. Boll, "Suppression of acoustic noise in speech using spectral subtraction," IEEE Trans. Acoust., Speech, Signal Process., vol. ASSP-27, no. 2, pp. 113-120, 1979.
4 J. Sohn, N. S. Kim and W. Sung, "A statistical model-based voice activity detection" IEEE Signal Processing Letters, vol. 6, no. 1, pp. 1-3, 1999.
5 R. Martin. "Spectral subtraction based on minimum statistics," in Proc., vol. ASSP-27, no. 2, pp. 113-120, 1979.
6 I. Cohen and B. Berdugo, "Speech enhancement for nonstationary noise environments," Signal Processing, vol. 81, pp. 2403-2418, 2001.   DOI   ScienceOn
7 G. Doblinger, "Computationally efficient speech enhancement by spectral minima tracking in subbands," in Proc. 4th EUROSPEECH'95, Madrid, Spain, pp. 1513-1516, 1995.
8 N. S. Kim and J. H. Chang, "Spectral enhancement based on global soft decision," IEEE Signal Processing Letters, pp. 108-110, 2000.
9 J. Meyer, K. U. Simmer and K. D. Kammeter, "Comparison of one-and two channel noise-estimation techniques," in Proc. 5th IWAENC'97, London, U.K, pp. 137-145, 1997.
10 R. J. McAualy and M. L. Malpass, "Speech enhancement using a soft-decision noise suppression filter," IEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP-28, pp. 137-145, 1980.
11 D. Malah, R. Cpx, and A. Accardi, "Tracking speech presence uncertainty to improve speech enhancement in non-stationary noise environments," Proc. IEEE Int. Conf. Acoustics, Speech, and Signal Processing, pp. 789-792, 1999.
12 G. Xuan, W.Zhang, and P. Chai, "EM algorithm of Gaussian mixture model and hidden Markov model," Proc. IEEE International Conference on Image Processing, vol. 1, pp. 145-148, 2001.
13 D. A. Reynolds, T. F. Quatieri, and R. B. Dunn, "Speaker verification using adapted Gaussian mixture models," Digital Signal Processing, vol. 10, pp. 19-41, 2000.   DOI   ScienceOn
14 ITU-T P.862, Perceptual evaluation of speech quality (PESQ), an objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs, Feb. 2001.
15 Y. Hu and P. C. Loizou, "Evaluation of objective quality measures for speech enhancement, " IEEE Transactions on Audio, Speech and Language Processing, pp. 229-238, 2008.