Performance enhancement of SSC sound source localization for indoor environment

The steered response power-phase transform algorithm (SRP-PHAT) has been widely utilized for robust sound source localization for indoor environment. Searching space clustering algorithm (SSC) is the improved version of SRP-PHAT, in which the computational complexity could be greatly reduced via the space division and clustering. However, SSC has to frequently perform the space division and clustering when the positions of microphone arrays are changed, which will induce additional computational complexity. In this paper, we proposed a coarse-to-fine region contraction SSC (CFRC-SSC) method to reduce the computational complexity of SSC for the sound source localization algorithm. The coarse level SSC with limited computational complexity will contract the whole searching space to several candidate spaces with limited size, which will reduce the searching volume for fine level SSC without omitting the actual sound source localization. Simulation results demonstrate that the proposed CFRC-SSC show a lower computational complexity in terms of SRP function evaluation times and space clustering calculation times compared to SSC.