Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17809
Title: Improved speech presence probability estimation based on wavelet denoising
Authors: Lun, DPK 
Shen, TW
Hsung, TC
Ho, DKC
Issue Date: 2012
Publisher: IEEE
Source: 2012 IEEE International Symposium on Circuits and Systems (ISCAS), 20-23 May 2012, Seoul, Korea (South), p. 1018-1021 How to cite?
Abstract: A reliable estimator for speech presence probability (SPP) can significantly improve the performance of many speech enhancement algorithms. Previous work showed that a good SPP estimator can be obtained by using a smooth a-posteriori signal to noise ratio (SNR) function, which can be achieved by reducing the noise variance when estimating the speech power spectrum. In this paper, a wavelet based denoising algorithm is proposed for such purpose. We first apply the wavelet transform to the periodogram of a noisy speech signal to generate an oracle for indicating the locations of the noise floor in the periodogram. We then make use of that oracle to selectively remove the wavelet coefficients of the noise floor in the log multitaper spectrum (MTS) of the noisy speech. The remaining wavelet coefficients are then used to reconstruct a denoised MTS and in turn generate a smooth a-posteriori SNR function. Simulation results show that the new SPP estimator outperforms the traditional approaches and enables a significantly improvement in the quality and intelligibility of the enhanced speeches.
URI: http://hdl.handle.net/10397/17809
ISBN: 978-1-4673-0218-0
ISSN: 0271-4302
DOI: 10.1109/ISCAS.2012.6271400
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