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Vol. 159, Issue 11, November 2013, pp. 218-225

 

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Blind Separation of Noisy Mixed Speech Based on Wiener Filtering and Independent Component Analysis
 
Hongyan Li, Xueying Zhang

College of Information Engineering of Taiyuan University of Technology, 030024, China
Tel.: 13934639420
E-mail: tylihy@163.com

 

Received: 25 July 2013   /Accepted: 25 October 2013   /Published: 30 November 2013

Digital Sensors and Sensor Sysstems

 

Abstract: Blind source separation problem has recently received a great deal of attention in signal processing and unsupervised neural learning. In the current approaches, the additive noise is negligible so that it can be omitted from the consideration. To be applicable in realistic scenarios, blind source separation approaches should deal evenly with the presence of noise. In this contribution, a novel noisy multiple channels blind signal separation algorithm was presented by wiener filtering and independent component analysis (ICA) when the measured signals were contaminated by additive noise. An improved wiener filtering algorithm was proposed to reduce the noise and then the FASTICA algorithm was used to separate the denoised speech. The results show that this method may reduce the affect of noise and improve the signal-noise ratio (SNR) of separation speech, accordingly renew the original speech.

 

Keywords: Independent component analysis, Blind sources separation, Wiener filtering, Speech enhancement, Noise reduction.

 

 

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