关键词: 语音增强, 注意力机制, 复数卷积网络, 编码, 长短期记忆

Abstract: Aiming at the problems of limited representation of graph feature correlation information and unsatisfactory denoising effect in existing speech enhancement methods, a Double Complex Convolutional Attention Aggregation Recurrent Network (DCCARN) was proposed. First, a double-complex convolutional network was established to encode the two-branch information of the spectrogram features after the short-time Fourier transform. Secondly, the encoders in the two branches were respectively used for different feature-block and intra-block attention mechanisms, and speech feature information was re-labeled. Then, the long-term sequence information was processed by Long-Short-Term-Memory (LSTM), and the spectral features were restored and aggregated by two decoders. Finally, the estimated speech waveform was generated by short-time inverse Fourier transform to activate the purpose of suppressing noise. Experiments are carried out on the public dataset Voice Bank + DMAND (VBD) and the noised the DARPA TIMIT Acoustic-Phonetic Continuous Speech Corpus（TIMIT）dataset. The results show that, compared with the phase-aware Deep Complex Convolutional Recurrent Network (DCCRN), DCCARN is more effective in Perceptual Evaluation of Speech Quality (PESQ) increased by 5.597% and 2.672% respectively. It is verified that the proposed method can more accurately capture the correlation information on the speech features, suppress noise more effectively and enhance speech intelligibility.

Key words: speech enhancement, attention mechanism, complex convolutional network, coding, LSTM(Long Short Term Memory)