A method of multifeatured full convolutionalneural network based on speech enhancement in airground voice communication

Abstract:

In order to study speech enhancement in the air traffic control (ATC) and save storage resources, a new speech enhancement method is proposed. Based on Fully Convolutional Networks (FCN), Skip connection is added and secondary features are introduced for joint learning. Specifically, the logpower spectra(LPS) of speech is used as the main training feature, and the logarithmic MelFrequency Cepstrum (LMFCC) is introduced as the secondary training feature to jointly optimizeparameters of FCN. Experiments have shown that the network architecture combining LPS and LMFCC has better speech enhancement performances than that with single LPS feature, and the LMFCC as a secondary feature can also be used for other purposes. Experiments also show that the addition of skip connections can improve the FCN network performances, and the new network structure has better performances with the same number of parametersthan the baseline deep neural network (DNN) method.

Key words:Speech Enhancement; Speech Separation; Full Convolutional Neural Network; Air-Ground Communication; Multi-featured Joint Learning