A Noise Reduction Method Based on LMS Adaptive Filter of Audio Signals

DOI

10.2991/icmt-13.2013.123How to use a DOI?

Keywords

Audio signals Noise reduction Adaptive signal processing LMS filter

Abstract

Noise reduction of audio signals is a key challenge problem in speech enhancement, speech recognition and speech communication applications, etc. It has attracted a considerable amount of research attention over past several decades. The most widely used method is optimal linear filtering method, which achieves clean audio estimate by passing the noise observation through an optimal linear filter or transformation. The representative algorithms include Wiener filtering, Kalman filtering, spectral restoration, subspace method, etc. Many theoretical analysis and experiments have been carried out to show that the optimal filtering technique can reduce the level of noise that is present in the audio signals and improve the corresponding signal-to-noise ratio (SNR). However, one of the main problems for optimal filtering method is complexity of the algorithm which based upon SVD–decompositions or QR–decompositions. In almost real signal applications it difficult to implement. In this paper, a method for reducing noise from audio or speech signals using LMS adaptive filtering algorithm is proposed. The signal is filtered in the time domain, while the filter coefficients are calculated adaptively by steepest-descent algorithm. The simulation results exhibit a higher quality of the processed signal than unprocessed signal in the noise situation.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Liu Yang
AU  - Xiao Mingli
AU  - Tie Yong
PY  - 2013/11
DA  - 2013/11
TI  - A Noise Reduction Method Based on LMS Adaptive Filter of Audio Signals
BT  - Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)
PB  - Atlantis Press
SP  - 994
EP  - 1001
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmt-13.2013.123
DO  - 10.2991/icmt-13.2013.123
ID  - Yang2013/11
ER  -