Multimodal blind source separation for moving sources based on robust beamforming

An improvement in the multimodal approach to the problem of blind source separation (BSS) of moving sources is proposed. The challenge of BSS for moving sources is that the mixing filters are time varying. Thus the unmixing filters should also be time varying, which are difficult to calculate from only statistical information avail able from limited number of audio samples. Therefore, in the pro posed approach a robust least square frequency invariant data independent (RLSFIDI) beamformer is implemented to perform real time speech enhancement and provide separation of the moving sources. Direction of arrival information of the sources is obtained from the visual 3-D tracker based on a Markov Chain Monte Carlo particle filter (MCMC-PF). The uncertainties in source localization and direction of arrival information are controlled by using convex optimization approach in the beamformer design. This provides robust ness with a wider main lobe for source of interest (SOI) and wider attenuation pattern to block the interference. In addition, white noise gain (WNG) constraint is used to control the beamformer sensitivity. Experimental results show that by utilizing RLSFIDI beamformer a significant improvement in BSS performance for moving sources is achieved in a low reverberant environment.