Novel mixing matrix estimation approach in underdetermined blind source separation

This paper proposes the use of density-based spatial clustering of application with noise (DBSCAN) and the Hough transform to estimate the mixing matrix in underdetermined blind source separation. First, phase-angle-based single source time-frequency point detection is employed to improve signal sparsity. To overcome the limitation of the K-means clustering algorithm, which requires prior knowledge of the number of sources, the DBSCAN classification algorithm is adopted to automatically estimate the number of sources and then further estimate the mixing matrix. The Hough transform is employed to modify the cluster center in order to enhance the estimation accuracy of the mixing matrix. Simulation results show that the proposed approach can effectively estimate the number of sources and the mixing matrix with high accuracy. The proposed approach performs better than the K-means method and the DBSCAN algorithm alone.