Microphone Clustering and BP Network based Acoustic Source Localization in Distributed Microphone Arrays

Abstract —A microphone clustering and back propagation (BP) neural network based acoustic source localization method using distributed microphone arrays in an inte lligent meeting room is proposed. In the propos ed method, a novel clustering method is first used to divide all microphones into several clusters where each one corresponds to a specified BP network. Afterwards, the energy-based cluster selecting scheme is applied to the select the clusters which are small and close to the acoustic source. In each chosen cluster, the time difference of arrival of each microphone pair is estimated, and then all estimated time delays act as input of the corresponding BP network for position estimation. Finally, all estimated positions from the chosen clusters are fused for global position estimation. Only subsets rather than all the microphones are responsible for acoustic source localization, which leads to less computational cost; moreover, the local estimation in each chosen cluster can be processed in parallel, which expects to improve the localization speed potentially. Simulation results from comparison with other related localization methods confirm the validity of the proposed method. Index Terms—acoustic source localization, BP neural network, microphone clustering, GCC-PHAT, TDOA.