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Sparse NMF based speech enhancement with bases update

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Abstract

In this paper, a combination of methods based on statistical modelling and Non-negative Matrix Factorization (NMF) for speech enhancement using speech and noise bases with on-line update is proposed. Template-based approaches are known to be more robust in the presence of non-stationary noises than methods based on statistical modeling. However, template-based approaches depend on a-priori information. The drawbacks of both the approaches can be avoided by combining them. In NMF approach, speech bases and noise bases are simultaneously adapted to further improve the performance. The proposed method outperforms other benchmark algorithms in terms of perceptual evaluation of speech quality (PESQ) and source-to-distortion ratio (SDR) in stationary and non-stationary noise environment conditions with matched and mismatched noise basis.

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Authors and Affiliations

  1. Computer Science Department, University of Crete, Heraklion, Greece

    V. Sunnydayal

  2. DSP Multimedia, TBU, TATA ELXSI LIMITED, Bangalore, India

    N. Siva Prasad & N. K. Ragesh

  3. Department of ECE, Vignan University, Guntur, India

    S. Ravishankar

  4. Electronics and Communication Department, National Institute of Technology Warangal, Warangal, India

    S. Surendran

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  1. V. Sunnydayal
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  2. N. Siva Prasad
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  3. S. Ravishankar
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  4. S. Surendran
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  5. N. K. Ragesh
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Correspondence to V. Sunnydayal.

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Sunnydayal, V., Siva Prasad, N., Ravishankar, S. et al. Sparse NMF based speech enhancement with bases update. Int J Speech Technol 20, 443–454 (2017). https://doi.org/10.1007/s10772-017-9418-0

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  • DOI: https://doi.org/10.1007/s10772-017-9418-0

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