Blind dereverberation of speech signals using independence transform matrix

In the real room environment, sound source is distorted with delayed versions of itself reflected from walls. This room reverberation severely degrades the intelligibility of speech and performance of automatic speech recognition system. Blind deconvolution is to find the inverse of reverberation channel when only convolved versions of the sources are available at the receiver. However existing blind deconvolution algorithms assume that a source signal has an independent identically-distributed (IED) non-Gaussian probability density function (PDF). In this research, colored nonstationary non-IID speech signals were transformed into an IID-like signal as possible by ICA-based independence transform and the resulting signals were processed using infomax blind deconvolution algorithm for the simulated minimum-phase finite impulse response (FIR) channels. Compared to the pre-whitening method by Torkkola, the proposed method demonstrated much better performance of about 30dB signal-to-reverberant components ratio (SRR).