Multimodal blind source separation for moving sources

A novel multimodal approach is proposed to solve the problem of blind source separation (BSS) of moving sources. 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 track in real time. In the proposed approach, the visual modality is utilized to facilitate the separation for both stationary and moving sources. The movement of the sources is detected by a 3-D tracker based on particle filtering. The full BSS solution is formed by integrating a frequency domain blind source separation algorithm and beamforming: if the sources are identified as stationary, a frequency domain BSS algorithm is implemented with an initialization derived from the visual information. Once the sources are moving, a beamforming algorithm is used to perform real time speech enhancement and provide separation of the sources. Experimental results show that by utilizing the visual modality, the proposed algorithm can not only improve the performance of the BSS algorithm and mitigate the permutation problem for stationary sources, but also provide a good BSS performance for moving sources in a low reverberant environment