Compressive Sensing-Based Sound Source Localization for Microphone Arrays

Abstract

Sound source localization with less data is a challenging task. To address this problem, a novel sound source localization method based on compressive sensing theory is proposed in this paper. Specifically, a sparsity basis is first constructed for each microphone by shifting the audio signal recorded from one reference microphone. In this manner, the microphones except the reference one are allowed to capture audio signals under the sampling rate far below the Nyquist criterion. Next, the source positions are estimated by solving an $$l_1$$ minimization based on each frame of audio signals. Finally, a fine localization scheme is presented by fusing the estimated source positions from multiple frames. The proposed method can directly determine the number of sound sources in one step and successfully estimate the source positions in noisy and reverberant environments. Experimental results demonstrate the validity of the proposed method.