ICCRN: Inplace Cepstral Convolutional Recurrent Neural Network for Monaural Speech Enhancement
Jinjiang Liu, Xueliang Zhang · 2023
According to the mechanism of speech production, speech can be decomposed into excitation and vocal tract which are sparsely represented in cepstral domain. In this study, we propose a neural network for monaural speech enhancement on time-frequency cepstral space that is implemented by inserting a cepstral frequency block into our inplace convolutional recurrent network. The proposed method has a good ability of restoring the speech masked by noise. Experimental results show that the proposed ICCRN model significantly outperforms the baseline system, particularly under low SNR conditions.