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Multimodal soft nonnegative matrix go-factorization for convolutive source separation

Sedighin, F ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/TSP.2017.2679692
  3. Abstract:
  4. In this paper, the problem of convolutive source separation via multimodal soft Nonnegative Matrix Co-Factorization (NMCF) is addressed. Different aspects of a phenomenon may be recorded by sensors of different types (e.g., audio and video of human speech), and each of these recorded signals is called a modality. Since the underlying phenomenon of the modalities is the same, they have some similarities. Especially, they usually have similar time changes. It means that changes in one of them usually correspond to changes in the other one. So their active or inactive periods are usually similar. Assuming this similarity, it is expected that the activation coefficient matrices of their Nonnegative Matrix Factorization (NMF) have a similar form. In this paper, the similarity of the activation coefficient matrices between the modalities is considered for co-factorization. This similarity is used for separation procedure in a soft manner by using penalty terms. This results in more flexibility in the separation procedure. Simulation results and comparison with state-of-the-art algorithms show the effectiveness of the proposed algorithm. © 1991-2012 IEEE
  5. Keywords:
  6. Audio-visual speech separation ; Nonnegative matrix co-factorization ; Blind source separation ; Chemical activation ; Factorization ; Speech analysis ; Audio-visual speech ; Coefficient matrix ; Convolutive mixture ; Convolutive source separation ; Multi-modality ; Non-negative matrix ; Nonnegative matrix factorization ; State-of-the-art algorithms ; Matrix algebra
  7. Source: IEEE Transactions on Signal Processing ; Volume 65, Issue 12 , 2017 , Pages 3179-3190 ; 1053587X (ISSN)
  8. URL: https://ieeexplore.ieee.org/document/7874217