Full convolutional speech enhance network based on with combined amplitude and phase spectra
Xingda Li · Applied and Computational Engineering · 2023
With the coming of the information age, speech has a pivotal role in modern society, and the study of speech enhancement has become necessary. To implement the often-neglected speech phase spectrum. This research proposes a full convolutional model based on an enhanced U-Net. The standard convolution kernel in the model is replaced with a deformable convolution with adaptive learning bias, a double attention mechanism is added between the encoder and decoder layers, and many residual connections are used to link the different layers, and the phase spectrum is also used as one of the inputs. The simulation experiments show that the added modules all contribute to the speech enhancement effect. Among them, the addition of the deformable convolution has the greatest contribution to the model.