Archives of Acoustics, 41, 3, pp. 579–590, 2016
10.1515/aoa-2016-0056

Wavelet Packet Transform based Speech Enhancement via Two-Dimensional SPP Estimator with Generalized Gamma Priors

Pengfei SUN
Southern Illinois University Carbondale
United States

Jun QIN
http://www.engr.siu.edu/~jqin/
Southern Illinois University Carbondale
United States

Although various speech enhancement techniques have been developed for different applications, existing methods are limited in noisy environments with high ambient noise levels. Speech presence probability (SPP) estimation is a speech enhancement technique to reduce speech distortions, especially for low signal-to-noise ratios (SNRs) scenario. In this paper, we propose a new two-dimensional (2D) Teager energy operators (TEOs) improved SPP estimator for speech enhancement in time-frequency (T-F) domain. Wavelet packet transform (WPT) as a multiband decomposition technique is used to concentrate the energy distribution of speech components. A minimum mean-square error (MMSE) estimator is obtained based on the generalized gamma distribution speech model in WPT domain. In addition, the speech samples corrupted by environment and occupational noise (i.e., machine shop, factory and station) at different input SNRs are used to validate the proposed algorithm. Results suggest that the proposed method achieves a significant enhancement on perceptual quality, compared with four conventional speech enhancement algorithms (i.e., MMSE-84, MMSE-04, Wiener-96, and BTW).
Keywords: speech enhancement; speech presence probability; wavelet packet transform; two-dimensional Teager energy operator.
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Copyright © Polish Academy of Sciences & Institute of Fundamental Technological Research (IPPT PAN).

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DOI: 10.1515/aoa-2016-0056