Abstract: In view of the difficulty of separating the singing from background music, a two-step accompaniment separation model based on robust principal component analysis and Mel frequency cepstrum coefficient repeated structure is proposed in this paper. The model effectively improves the problems of incomplete song separation and poor separation of Mel frequency cepstrum coefficients at low frequencies existed in robust principal component analysis. Firstly, the mixed music is decomposed into low rank matrix and sparse matrix by robust principal component analysis, then the characteristic parameters of Mel frequency cepstrum coefficients are extracted and the similar operations are carried out. The similarity matrix and the repeated structure model of Mel frequency cepstrum coefficients are constructed, and by the repeated structure model, both the low rank matrix related masking matrix and the sparse matrix related masking matrix are obtained. Finally, the background music and singing are obtained through the masking matrices and inverse Fourier transform. Experiments are carried out on public data sets. Compared with the existing comparison algorithm, the average signal to interference ratio of the proposed algorithm is improved by 7 dB.