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.
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Year 2013,
Volume: 13 Issue: 1, 1589 - 1595, 02.09.2013
Jahangir Alam
,
Faqrul Alam Chowdhury
Fasiul Alam
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.
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- 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.
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- 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.
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- 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.