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A multimodal approach to blind source separation of moving sources

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journal contribution
posted on 2010-11-19, 09:12 authored by Mohsen Naqvi, Miao Yu, Jonathon Chambers
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 calculate 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 video cameras. Positions and velocities of the sources are obtained from the 3-D tracker based on a Markov Chain Monte Carlo particle filter (MCMC-PF), which results in high sampling efficiency. The full BSS solution is formed by integrating a frequency domain blind source separation algorithm and beamforming: if the sources are identified as stationary for a certain minimum period, a frequency domain BSS algorithm is implemented with an initialization derived from the positions of the source signals. Once the sources are moving, a beamforming algorithm which requires no prior statistical knowledge is used to perform real time speech enhancement and provide separation of the sources. Experimental results confirm that by utilizing the visual modality, the proposed algorithm not only improves the performance of the BSS algorithm and mitigates the permutation problem for stationary sources, but also provides a good BSS performance for moving sources in a low reverberant environment.

History

School

  • Mechanical, Electrical and Manufacturing Engineering

Citation

NAQVI, S.M., YU, M. and CHAMBERS, J.A., 2010. A multimodal approach to blind source separation of moving sources. IEEE Journal of selected topics in signal processing, 4(5), pp 895- 910

Publisher

© IEEE

Version

  • VoR (Version of Record)

Publication date

2010

Notes

This is a journal article[© IEEE]. It is also available at: http://ieeexplore.ieee.org/ Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

ISSN

1932-4553

Language

  • en