Multi-Source Localization Using Optimized Time-Frequency Representation and Sparsity Component Analysis.

This article aims to address the multi-source localization problem by exploiting the sparsity of the speech signal in the time-frequency domain, where the challenge mainly lies in extracting the sparse component. An optimized time-frequency representation and sparsity component analysis-based multi-source localization method is proposed to overcome this challenge. Firstly, extracting the sparse components relies on the accurate representation in the time-frequency domain. However, the energy leakage problem caused by linear time-frequency transformation limits the accuracy of sparse component extraction. To tackle this problem, inspired by empirical mode decomposition, the proposed method classifies all the points in the time-frequency domain into four categories based on their phase feature and mode characteristics. Each type of the point is modeled separately, and a point-by-point analysis is conducted to remove all the points affected by energy leakage. Then, based on the optimized time-frequency representation, the phase coherence criterion is used to detect the sparse component in the point level. Following that, guided by the mode consistency characteristic of sparse components, an extension scheme is proposed to recover the falsely removed sparse components. Finally, the detected sparse components are applied for the multiple source localization. The objective evaluation is performed in both simulation and actual recording environments, and the proposed method can achieve better localization accuracy compared to several existing methods.