This paper deals with the blind separation of nonstationary sources and directionofarrival(DOA) estimation in the underdetermined case, when there are more sources than sensors. We assume the sources to be timefrequency(TF) disjoint to a certain extent. In particular, the number of sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. Compared with the subspacebased algorithm under the same sparseness assumption, which suffers from the extra noise effect since it cannot estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their corresponding TF values in any TF neighborhood simultaneously. Another contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering technique. Simulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation(BSS) and DOA estimation. This paper deals with the blind separation of nonstationary sources and direction ofarrival (DOA) estimation in the underdetermined case, when there are more sources than sensors. We assume the sources to be timefrequency (TF) disjoint to a certain extent. of the sources presented at any TF neighborhood is strictly less than that of sensors. We can identify the real number of active sources and achieve separation in any TF neighborhood by the sparse representation method. which compared with the sparseness assumption method, which suffers from the extra noise effect since it can not estimate the true number of active sources, the proposed algorithm can estimate the number of active sources and their corresponding TF values ​​in any TF neighborhood simultaneously. Another contribution of this paper is a new estimation procedure for the DOA of sources in the underdetermined case, which combines the TF sparseness of sources and the clustering tech nique. Simulation results demonstrate the validity and high performance of the proposed algorithm in both blind source separation (BSS) and DOA estimation.