Indoor Multiple Sound Source Localization via Multi-Dimensional Assignment Data Association

Abstract

In this paper, we address the multiple sound source localization problem by associating and fusing the direction of arrival DOA estimates from multiple microphone arrays. For multi-source scenarios especially in indoor environments, a critical issue is to tell the correspondence among DOA estimates across different arrays, which is known as the data association problem. We propose a multi-dimensional assignment-based data association approach to find the optimal associations of DOA estimates from the same source. First, in the sense of maximum likelihood, the data association problem is formulated by finding the most likely partition of the measurement set into the source-originated and false alarm-originated subsets. Next, by defining the association costs appropriately, the problem of finding the most likely measurement partition is transformed into a generalized multi-dimensional assignment problem which can be solved efficiently by a Lagrangian relaxation algorithm. After the optimal associations of DOA estimates across different arrays are obtained, the locations of sources can be estimated by fusing the same source-originated DOA estimates. In the presence of missed detections, false alarms and the unknown number of sources, the proposed method achieves high accuracy in data association and localization, and outperforms the competing method in reverberant and noisy environments. In addition, since our method does not require additional features and uses DOA estimates only, it is more computationally efficient than the competing method.