Abstract: To improve the performance of sound source localization based on distributed microphone arrays in noisy and reverberant environments, a sound source localization method was proposed. This method exploited the inherent spatial sparsity to convert the localization problem into a sparse recovery problem based on the compressive sensing (CS) theory. In this method two-step discrete cosine transform (DCT)-based feature extraction was utilized to cover both short-time and long-time properties of the signal and reduce the dimensions of the sparse model. Moreover, an online dictionary learning (DL) method was used to dynamically adjust the dictionary for matching the changes of acoustic signals, and then the sparse solution could better represent location estimations. In addition, we proposed an improved approximate 1₀ norm minimization algorithm to enhance reconstruction performance for sparse signals in the low signal-noise ratio (SNR) condition. The effectiveness of the proposed scheme is demonstrated by simulation results where the locations of multiple sources can be obtained in the noisy and reverberant conditions.