Local Sparsity Based Online Dictionary Learning for Environment-Adaptive Speech Enhancement with Nonnegative Matrix Factorization

In this paper, a nonnegative matrix factorization (NMF)-based speech enhancement method robust to real and diverse noise is proposed by online NMF dictionary learning without relying on prior knowledge of noise. Conventional NMF-based methods have used a fixed noise dictionary, which often results in performance degradation when the NMF noise dictionary cannot cover noise types that occur in real-life recording. Thus, the noise dictionary needs to be learned from noises according to the variation of recording environments. To this end, the proposed method first estimates noise spectra and then performs online noise dictionary learning by a discriminative NMF learning framework. In particular, the noise spectra are estimated from minimum mean squared error filtering, which is based on the local sparsity defined by a posteriori signal-to-noise ratio (SNR) estimated from the NMF separation of the previous analysis frame. The effectiveness of the proposed speech enhancement method is demonstrated by adding six different realistic noises to clean speech signals with various SNRs Consequently, it is shown that the proposed method outperforms comparative methods in terms of signal-to-distortion ratio (SDR) and perceptual evaluation of speech quality (PESQ) for all kinds of simulated noise and SNR conditions.