Singer Identification of Pop Music with Singing-voice Separation by RPCA

Singer identification is important in music organizing and retrieving. However, in many cases, the correct rate of singer identification system is not high enough. In this paper, we propose an effective system of singer identification with human voice separated from original music. The first part of this research is music separation, and we would like to use Robust principal component analysis (RPCA) to solve this problem with its high performance. After the clear enough human voices are extracted, we can proceed to the second part, singer identification. At this stage, the Linear Predictive Coding (LPC) method was chosen as the experimental method. When we finish extracting the LPC features, the singer would be identified by Gaussian Mixture Model (GMM). MATLAB is used to the singer identification algorithm