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The underdetermined blind source separation (BSS), which based on sparse representation, is discussed in this paper; moreover, some difficulties (or real assumptions) that were left out of consideration before are aimed. For instance, the number of sources, , is unknown, large-scale, or time-variant; the mixing matrix is ill-conditioned. For the proposed algorithm, in order to detect a time-variant mixing matrix, short-time Fourier transform is employed to segment received mixtures. Because is unknown, our algorithm use more estimates to find out the mixing vectors by particle swarm optimizer (PSO); and then, surplus estimates are removed by two proposed processes. However, the estimated accuracy of PSO will affect the correctness of extracting mixing vectors. Consequently, an improved PSO version called the cluster guide PSO (CGPSO) is further proposed according to the character of sparse representation. In simulations, several real assumptions that were less discussed before will be tested. Some representative BSS algorithms and PSO versions are compared with the CGPSO-based algorithm. The advantages of the proposed algorithm are demonstrated by simulation results.