Online Update of Noise Basis Matrix for the NMF-based Speech Enhancement

In nonnegative matrix factorization (NMF)-based speech enhancement, the matched noise basis matrix needs to be trained, which is difficult to be guaranteed in practice. In this paper, an NMF-based speech enhancement method is proposed in which the noise basis matrix is updated online. First, the non-speech regions of noisy signal are determined by utilizing a decision module of non-speech frame. Then, a fixed-length sliding window is used to cover several recent past frames of noisy speech determined as non-speech, and the magnitude spectrums of these non-speech frames are used to update the noise basis matrix online. After that, the updated noise basis matrix and the pre-trained speech basis matrix are used to achieve speech enhancement. This method can obtain the matched noise basis matrix online and effectively solve the problem of the mismatch of the noise basis matrix. The test results demonstrate that the noise basis matrix trained online by the proposed method performs better than that trained from the matched dataset in most conditions.