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JACIII Vol.8 No.6 pp. 613-620
doi: 10.20965/jaciii.2004.p0613
(2004)

Paper:

Single-Channel Noise Reduction for Multiple Background Noises Using Perceptual Wavelet Packet Transform and Fuzzy Logic

Montri Phothisonothai, Pinit Kumhom, and Kosin Chamnongthai

Computer Vision Laboratory & VLSI Laboratory, Department of Electronics and Telecommunication Engineering, King Mongkut’s University of Technology Thonburi, 91 Pracha-Utith Road, Trungkru, Bangmod, Bangkok 10140, Thailand

Received:
July 30, 2004
Accepted:
September 12, 2004
Published:
November 20, 2004
Keywords:
noise reduction, speech enhancement, additive noise, perceptual wavelet, fuzzy logic
Abstract

Background noises interfere with communication devices such as mobile telephone, digital hearing aid, etc. Therefore noise reduction (NR) part for limiting the effect of these noises is important. The paper proposes a noise reduction method based on the soft decision-making by the fuzzy inference system (FIS). The different characteristics of noises frequently occurring are used for creating the fuzzy decision rule base of the FIS. The FIS have two input parameters: the average energy and the difference of the average energy. The analysis of the FIS is done in the domain of the perceptual wavelet packet transform (PWPT) that is the human’s psychoacoustic model. The output of the FIS is used to modify the PWPT coefficients in such a way that it is more likely that the noise components are reduced while the speech signal is enhanced. The enhanced speech signal is the result of the inverse perceptual wavelet packet transform (IPWPT) of the modified coefficients. The experiment results show that the proposed method gives lower distortion than do the conventional methods especially when the input signal-to-noise ratio (SNR) is low; e.g. at SRN at 0dB the proposed method improves the output SNR level up to 4.18dB.

Cite this article as:
Montri Phothisonothai, Pinit Kumhom, and Kosin Chamnongthai, “Single-Channel Noise Reduction for Multiple Background Noises Using Perceptual Wavelet Packet Transform and Fuzzy Logic,” J. Adv. Comput. Intell. Intell. Inform., Vol.8, No.6, pp. 613-620, 2004.
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