基于幅值掩膜时频神经网络的语音频带扩展OA北大核心CSTPCD

To improve the performance of speech bandwidth extension based on deep learning,a time-frequency neural network model combined with amplitude mask was proposed.This model could not only exploit the phase information of speech,but also optimize the predicted speech amplitude through amplitude mask.In the time domain part of the model,a long short-term memory neural network integrating attention mechanism was designed.This network could realize parallel computing,and when predicting high-frequency speech,it could make full use of the relationship between the front and back speech frames with similar distance,and discard the learning of the relationship between the distant speech frames,thus reducing the calculation amount of the model.Subjective and objective experiments show that the method is superior to the traditional methods and the deep neural network based speech bandwidth extension methods in terms of signal to noise ratio and intelligibility.