Multi-source sound localization using the competitive k-means clustering

Sound source localization is an important part of intelligent robot auditory system. It makes a robot to respond naturally to human user´s call. In the ordinary situations, there always exist multiple sound sources including user´s call. Since localized outputs from each source are mixed in distribution, clustering is an important issue in the multi-source sound localization. In this work, we propose a new k-means clustering algorithm for unknown number of clusters, which is the competitive k-means. We compared its performance to the adaptive k-means++ algorithm and verified its effectiveness. Finally, we applied it to our sound source localization for multi-source sound localization and achieved satisfying results.