WIENER DENOISING BASED ON PERCEPTUAL FREQUENCY WEIGHTING AND NOISE SPECTRUM SHAPING

Year 2013, Volume: 13 Issue: 1, 1589 - 1595, 02.09.2013

Jahangir Alam Faqrul Alam Chowdhury Fasiul Alam

Abstract

Among the numerous noise reduction techniques that were developed over the past several decades, the Wiener filter can be considered as one of the most fundamental noise reduction approaches, which has been delineated in different forms and adopted in various applications. An important parameter of numerous speech enhancement algorithms is the a priori signal-to-noise ratio (SNR). The Wiener filter emphasizes portions of the noisy signal spectrum where SNR is high and attenuates portions of the spectrum where the SNR is low. An adaptive time varying filter can be used for whitening the noisy speech signal corrupted by narrow-band noise whereas by enhancing the signal using Perceptual frequency weighting filter (PFWF), formant regions of the noisy speech spectrum can be made less affected for a given SNR. Incorporation of PFWF and/or NSSF (Noise spectrum shaping filter) into the Weiner denoising technique improves the performance of the speech enhancement system.

Keywords

A priori SNR, Wiener filter, speech enhancement, perceptual weighting, noise spectreum shaping.

References

• Boll, S. F., “Suppression of acoustic noise in speech using spectral subtraction”, IEEE Trans. Acoustics, Speech, Signal Processing, vol. 27, pp. 113–120, Apr. 19 M. Berouti, R. Schwartz, and J. Makhoul, “Enhancement of speech corrupted by acoustic noise”, in Proc. IEEE Int. Conf. on Acoustics, Speech, Signal Processing, vol. 1, (Washington, DC), pp. 208–211, Apr. 1979.
• H. L. V. Trees, Detection, Estimation, and Modulation: Part I - Detection, Estimation and Linear Modulation Theory. John Wiley and Sons, Inc., 1st ed., 1968.
• T.F. Quatieri and R.B. Dunn, “Speech enhancement based on auditory spectral change,” IEEE Int. Conf. on Acoustics, Speech and Signal Processing, vol. 1, pp. 257-260, Orlando, FL, USA, 2002.
• Y.M. Cheng and D. O’Shaughnessy, “Speech enhancement based conceptually on auditory evidence,” IEEE Trans. Signal Processing, vol.39, no.9, pp.1943– 1954, 1991.
• D. Tsoukalas, M. Paraskevas, and J. Mourjopoulos, “Speech enhancement using psychoacoustic criteria,” IEEE ICASSP, pp.359–362, Minneapolis, MN, 1993.
• N. Virag, “Single channel speech enhancement based on masking properties of the human auditory system,” IEEE Trans. Speech Audio Processing, vol.7, no.2, pp.126– 137, 1999.
• M. R. Schroeder, B. S. Atal, and J. L. Hall, “Optimizing digital speech coders by exploiting masking properties of the human ear,” J. Acoust. Soc. Am., vol. 66, pp. 1647– 1652, Dec. 1979.
• E. Zwicker and H. Fastl, Psychoacoustics: Facts and Models. Springer-Verlag, 2nd ed., 1999.
• Y. Ephraim and D. Mallah, “Speech enhancement using a minimum mean-square error short-time spectral amplitude estimation,” IEEE Trans. Acoust. Speech, Signal Processing, vol. ASSP-32, no. 6, pp. 1109-1121, Dec. 1984.
• O. Cappe, “Elimination of the musical noise phenomenon with the Ephraim and Malah noise suppressor,” IEEE Trans. Speech and Audio Processing, vol. 2, no. 1, pp. 345–349, April 1994.
• Yi Hu and Philipos C. Loizou, “Evaluation of Objective Quality Measures for Speech Enhancement,” IEEE Trans. on Audio, Speech and Language Processing, vol. 16, No. 1, pp. 229-238, January 2008.
• Quackenbush S., T. Barnwell and M. Clements, Objective Measures of Speech Quality, Englewood Cliffs, NJ, USA, Prentice Hall, 1988.
• J. H. L. Hansen and B. L. Pellom, “An effective evaluation protocol for speech enhancement algorithms”, in Proc. ICSLP, vol. 7, Sydney, Australia, 1998, pp. 2819–2822.
• H. Hirsch and D. Pearce, “The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy environments”, ISCA ITRW ASR, September 2000.
• H. Tolba, Z. Li, and D. O’Shaughnessy, “Robust automatic speech recognition using a perceptually-based optimal spectral amplitude estimator speech enhancement algorithm in various low-SNR environments”, INTERSPEECH - Eurospeech, pp. 937– 940, September, 2005.
• Z. Li, H. Tolba, and D. O’Shaughnessy, “Robust automatic speech recognition using an optimal spectral amplitude estimator algorithm in low-SNR car environments”, INTERSPEECH - ICSLP, pp. 2041–2044, October 2004.
• Zili Li, "Distributed Speech Recognition and Speech Reconstruction System in Noisy Environments", Ph.D. thesis, Dept. of Telecomm., INRS-EMT, Univ. of Quebec, Montreal, Canada, 2007.