Efficient energy-based orthogonal matching pursuit algorithm for multiple sound source localization with unknown source count

Abstract

Abstract In this paper, we propose a compressed sensing (CS) sound source localization algorithm based on signal energy to solve the problem of stopping the iteration condition of the orthogonal matching pursuit (OMP) reconstruction algorithm in CS. The orthogonal matching tracking algorithm needs to stop iteration according to the number of sound sources or the change of residual. Generally, the number of sound sources cannot be known in advance, and the residual often leads to unnecessary calculation. Because the sound source is sparsely distributed in space, and its energy is concentrated and higher than that of the environmental noise, the comparison of the signal energy at different positions in each iteration reconstruction signal is used to determine whether the new sound source is added in this iteration. At the same time, the block sparsity is introduced by using multiple frequency points to avoid the problem of different iteration times for different frequency points in the same frame caused by the uneven energy distribution in the signal frequency domain. Simulation and experimental results show that the proposed algorithm retains the advantages of the orthogonal matching tracking sound source localization algorithm, and can complete the iteration well. Under the premise of not knowing the number of sound sources, the maximum error between the number of iterations and the set number of sound sources is 0.31. The experimental results show that the proposed algorithm has good positioning accuracy and has certain anti-reverberation capability. Compared with other OMP algorithms, the proposed algorithm has better iterative ability and stability. This work is helpful in promoting the development of multiple sound source localization.