Title：A separation method of singing and accompaniment combining discriminative training deep neural network

Author(s): ZHANG Tianqi; XIONG Mei; ZHANG Ting; YANG Qiang

Affiliation(s): Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications

Abstract: For the difficulty of separation between singing and accompaniment in the musical signals, an improved music separation method of based on discriminative training depth neural network(DNN) was proposed. Firstly, based on the DNN model, considering the reconstruction errors and discrimination information between singing and accompaniment, an improved objective function was presented to discriminate the training; Then, an additional layer was added to DNN model, introducing the time-frequency masking to optimize the estimated accompaniment of the song, and the corresponding time-domain signal was obtained by inverse Fourier transform; Finally, the influence of different parameters on the separation performance was verified, and compared it with the existing music separation methods. The experimental results showed that the improved objective function and the introduction of time-frequency masking significantly improved the separation performance of the DNN, and the separation performance was improved about 4 dB compared with other existing music separation methods, thus verifying that the proposed method was an effective music separation algorithm.