Application of Blind Source Separation in Speech Processing for Combined Interference Removal and Robust Speaker Detection Using a Two-microphone Setup

A speech enhancement scheme is presented integrating spatial and temporal signal processing methods for blind denoising in non stationary noise environments. In a first stage, spatially localized interferring point sources are separated from noisy speech signals recorded by two microphones using a Blind Source Separation (BSS) algorithm assuming no a priori knowledge about the sources involved. Spatially distributed background noise is removed in a second processing step. Here, the BSS output channel containing the desired speaker is filtered with a time-varying Wiener filter. Noise power estimates for the filter coefficients are computed from desired speaker absent time-intervals identified by comparing signal energy of separated source files from the BSS stage. The scheme’s performance is illustrated by speech recognition experiments on real recordings corrupted by babble noise and compared to conventional beamforming and single channel denoising techniques.