A multi-source localization method based on clustering and outlier removal

Multiple sound source localization is a hot topic of concern in recent years. In this paper, a multi-source localization method based on weight clustering and outlier removal is proposed to deal with the multiple source localization in the environment with high reverberation time. In this kind of environments, there are always some T-F points consisting of components from multiple sources mixed in the detected spares components. These T-F points, which are called outliers, usually carry the wrong information of localization and could lead to the decline of localization accuracy. To solve this problem, the Point Offset Residual Weight (PORW) and Source Offset Residual Weight (SORW) are introduced to measure the contribution of each T-F point to the localization. The binary clustering is proposed to distinguish and remove the outliers. After that, a statistical histogram of DOA estimation is drawn using the composite weight to weaken the effect of components that interfere with the localization. Finally, the multi-source localization is conducted through peak searching. The objective evaluation of the proposed method is conducted in various simulated environments. The results show that the proposed method achieves a better performance compared with the reference methods in sources localization.