Harmonic enhancement using learnable comb filter for light-weight full-band speech enhancement model

Xiaohuai Le; Tong Lei; Li Chen; Yiqing Guo; Chao He; Cheng Chen; Xianjun Xia; Hua Gao; Yijian Xiao; Piao Ding; Shenyi Song; Jing Lu

arXiv · 2306.00812

Harmonic enhancement using learnable comb filter for light-weight full-band speech enhancement model

Xiaohuai Le·Tong Lei·Li Chen·Yiqing Guo·Chao He·Cheng Chen·Xianjun Xia·Hua Gao·Yijian Xiao·Piao Ding·Shenyi Song·Jing Lu

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Abstract

With fewer feature dimensions, filter banks are often used in light-weight full-band speech enhancement models. In order to further enhance the coarse speech in the sub-band domain, it is necessary to apply a post-filtering for harmonic retrieval. The signal processing-based comb filters used in RNNoise and PercepNet have limited performance and may cause speech quality degradation due to inaccurate fundamental frequency estimation. To tackle this problem, we propose a learnable comb filter to enhance harmonics. Based on the sub-band model, we design a DNN-based fundamental frequency estimator to estimate the discrete fundamental frequencies and a comb filter for harmonic enhancement, which are trained via an end-to-end pattern. The experiments show the advantages of our proposed method over PecepNet and DeepFilterNet.

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Xiaohuai Le, Tong Lei, Li Chen, Yiqing Guo, Chao He, Cheng Chen, Xianjun Xia, Hua Gao, Yijian Xiao, Piao Ding, Shenyi Song, Jing Lu. 2023-06-01. Harmonic enhancement using learnable comb filter for light-weight full-band speech enhancement model. https://arxiv.org/abs/2306.00812

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