Blind Separation of Noisy Mixed Speech Signals Based Independent Component Analysis
Hongyan Li, Ren Guanglong · 2010
The research on blind source separation is a focus in the community of signal processing and has been developed in recent years. 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, we propose to independent component analysis (ICA) when the measured signals are contaminated by additive noise, a method based on single channel ICA speech enhancement algorithm and FASTICA algorithm is proposed to separate noisy mixed speech signals. We first use single channel ICA speech enhancement algorithm to de-noise for each mixed noisy speech and then use the FASTICA algorithm to separate the de-noised speech signals. The result shows that this method may reduce the affect of noise and improve the signal-noise ratio (SNR) of separation signal, accordingly renew the original speech signals preferably.