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Journal of Electrical and Computer Engineering
Volume 2012 (2012), Article ID 282019, 12 pages
http://dx.doi.org/10.1155/2012/282019
Research Article

Application of Perceptual Filtering Models to Noisy Speech Signals Enhancement

1LRSITI, Département Génie Electrique, Ecole Nationale des Ingénieurs de Tunis, BP 37, 1002 Le Belvédère, Tunisia
2Département de Génie Physique et Instrumentations, Institut National des Sciences Appliquées et de Technologies, Centre Urbain Nord, BP 676, 1080 Tunis Cedex, Tunisia

Received 20 March 2012; Revised 24 May 2012; Accepted 30 May 2012

Academic Editor: Raj Senani

Copyright © 2012 Novlene Zoghlami and Zied Lachiri. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper describes a new speech enhancement approach using perceptually based noise reduction. The proposed approach is based on the application of two perceptual filtering models to noisy speech signals: the gammatone and the gammachirp filter banks with nonlinear resolution according to the equivalent rectangular bandwidth (ERB) scale. The perceptual filtering gives a number of subbands that are individually spectral weighted and modified according to two different noise suppression rules. The importance of an accurate noise estimate is related to the reduction of the musical noise artifacts in the processed speech that appears after classic subtractive process. In this context, we use continuous noise estimation algorithms. The performance of the proposed approach is evaluated on speech signals corrupted by real-world noises. Using objective tests based on the perceptual quality PESQ score and the quality rating of signal distortion (SIG), noise distortion (BAK) and overall quality (OVRL), and subjective test based on the quality rating of automatic speech recognition (ASR), we demonstrate that our speech enhancement approach using filter banks modeling the human auditory system outperforms the conventional spectral modification algorithms to improve quality and intelligibility of the enhanced speech signal.