Speech enhancement using posterior regularized NMF with bases update

In this paper, a combination of statistical model-based approach and Non-negative Matrix Factorization (NMF)-based approach with on-line update of speech and noise bases for speech enhancement is proposed. Template-based approaches are more robust and perform better than non-stationary noises compared to statistical model-based approaches but are dependent on a priori information. Combining the approaches avoids the drawbacks of both. To improve the performance further, speech and noise bases are adapted simultaneously in NMF approach with the help of the estimated speech presence probability (SPP). 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.