An NMF-based MMSE Approach for Single Channel Speech Enhancement Using Densely Connected Convolutional Network

Xinyu Li, Changchun Bao, Zihao Cui

Abstract: Presently, because of the development of deep learning technology, there has been increasingly more attention on state-of-the-art masking and mapping based speech enhancement methods. However, traditional speech enhancement approaches, like minimum mean-square error (MMSE) and wiener filter (WF) have not been fully investigated. In order to the better characterize, we proposed a deep learning based MMSE approach for single-channel speech enhancement based on Non-negative Matrix Factorization (NMF). The performance of MMSE approach can be improved by a priori signal-to-noise ratio. Therefore, we utilized an NMF-based Densely Connected Convolutional Network (DenseNet) as an estimator of the a priori signal-to-noise ratio (SNR). In test stage, multiple SNR level speech from colored noise sources and real-world non-stationary noise sources were used for evaluation. As expected, our present study outperformed many previous speech enhancement methods.

External IDs:dblp:conf/icspcc/LiBC21