Archives of Acoustics, 45, 4, pp. 565–572, 2020
10.24425/aoa.2020.134072

Speech Enhancement Based on Discrete Wavelet Packet Transform and Itakura-Saito Nonnegative Matrix Factorisation

Houguang LIU
China Univerity of Mining and Technology
China

Wenbo WANG
China Univerity of Mining and Technology
China

Lin XUE
China Univerity of Mining and Technology
China

Jianhua YANG
China Univerity of Mining and Technology
China

Zhihua WANG
China Univerity of Mining and Technology
China

Chunli HUA
China Univerity of Mining and Technology
China

Nonnegative matrix factorization (NMF) is one of the most popular machine learning tools for speech enhancement (SE). However, there are two problems reducing the performance of the traditional NMFbased SE algorithms. One is related to the overlap-and-add operation used in the short time Fourier transform (STFT) based signal reconstruction, and the other is the Euclidean distance used commonly as an objective function; these methods can cause distortion in the SE process. In order to get over these shortcomings, we propose a novel SE joint framework which combines the discrete wavelet packet transform (DWPT) and the Itakura-Saito nonnegative matrix factorisation (ISNMF). In this approach, the speech signal was first split into a series of subband signals using the DWPT. Then, the ISNMF was used to enhance the speech for each subband signal. Finally, the inverse DWPT (IDWT) was utilised to reconstruct these enhanced speech subband signals. The experimental results show that the proposed joint framework effectively enhances the performance of speech enhancement and performs better in the unseen noise case compared to the traditional NMF methods.
Keywords: speech enhancement; discrete wavelet packet transform; nonnegative matrix factorisation; Itakura-Saito divergence
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DOI: 10.24425/aoa.2020.134072