Speech enhancement method based on multi-domain fusion and neural architecture search - Rui ZHANG; Pengyun ZHANG; Chaoli SUN (Tongxin xuebao)
In order to further improve the self-learning and noise reduction ability of speech enhancement model, a speech enhancement method based on multi-domain fusion and neural architecture search was proposed.The multi-spatial domain mapping and fusion mechanism of speech signals were designed to realize the mining of real complex number correlation.Based on the characteristics of convolution pooling of the model, a complex neural architecture search mechanism was proposed, and the speech enhancement model was constructed efficiently and automatically through the designed search space, search strategy and evaluation strategy.In the comparison and generalization experiment between the optimal speech enhancement model and the baseline model, the two indexes of PESQ and STOI increase by 5.6% compared with the optimal baseline model, and the number of model parameters is the lowest.