Recurrent Neural Network and Bionic Wavelet Transform for speech enhancement

被引：3

作者：

Mourad, Talbi ^{[1
]}

Lotfi, Salhi ^{[1
]}

Sabeur, Abid ^{[1
]}

Adnane, Cherif ^{[1
]}

机构：

[1] Univ Campus, Fac Sci Tunis, Lab Signal Proc, 2092 El Manar II, Tunis, Tunisia

来源：

INTERNATIONAL JOURNAL OF SIGNAL AND IMAGING SYSTEMS ENGINEERING | 2010年 / 3卷 / 02期

关键词：

scales; envelope; perceptual model; cochlear filter bank; Bionic wavelet transform; mean opinion score; Elman neural network;

D O I：

10.1504/IJSISE.2010.035002

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper deals with speech enhancement using Bionic Wavelet Transform (BWT) and Recurrent Neural Network (RNN). Indeed, it describes a new technique, which removes additive background noise from noisy speech. This technique can be divided into two stages: the application of BWT to the speech signals and the application of an Elman neural network to find an optimal thresholding set to remove related noise wavelet coefficients. Simulation results obtained from computation of the Signal to Noise Ratio (SNR) and the Mean Opinion Scores (MOSs) show good performance of the proposed technique in comparison with many other methods.

引用

页码：136 / 144

页数：9

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