Optimization of EVS speech/music classifier based on deep learning

EVS (Enhanced Voice Services) is a multi-mode codec proposed by 3GPP (3rd Generation Partnership Project) for 4G mobile services with a good performance and codec quality. The key technology of EVS lies in the flexible switch between speech and audio coding mode which mostly depends on the speech/music classifier. In general, the music signal is more complex than speech signal, and it conform less to any known LP (Linear Prediction)-based model. Taking the EVS's internal classifier as a baseline system, this study presents the optimization of the speech/music classifier from the perspective of neural network. The paper demonstrates the effectiveness of the optimized system on the MUSAN database. The experimental results show that the optimized system can improve the performance of the classifier, especially for music classification. Performed subjective experiments indicate that the proposed classification architecture improves perceived audio quality of the EVS codec.