A Priori SNR Estimation Using Discriminative Non-Negative Matrix Factorization

A priori signal-to-noise ratio (SNR) contains critical information about the single-channel mixture of a speech and noise signal, and can be used by speech enhancement algorithms. In this paper, we propose a novel a priori SNR estimator using the estimates obtained from discriminative non-negative matrix factorization (DNMF). The idea of our new approach is to utilize the DNMF to perform the preliminary speech components estimation, which can be either directly used to estimate the a priori SNR, or can be combined with the well-known decision-directed (DD) approach by Ephraim and Malah to perform the a priori SNR estimation. We present a speaker-independent but noise-dependent DNMF-based a priori SNR estimator. Speech enhancement simulation results in the presence of non-stationary noise validate our new approach combined with well-known spectral weighting rules, outperforming several NMF-based and non-NMF-based state-of-the-art methods, w.r.t. both SNR improvement and speech perceptual quality.

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