An iteratively reweighted steered response power approach to multisource localization using a distributed microphone network.

The steered response power (SRP) with phase transform algorithm has been demonstrated to be robust against reverberation and noise for single-source localization. However, when this algorithm is applied to multisource localization (MSL), the "peak missing problem" can occur, namely, that some sources dominate over others over short time intervals, resulting in fewer significant SRP peaks being found than the true number of sources. This problem makes it difficult to detect all the sources among the available SRP peaks. We propose an iteratively reweighted steered response power (IR-SRP) approach that effectively solves the "peak missing problem" and achieves robust MSL in reverberant noisy environments. The initial IR-SRP localization function is computed over the time-frequency (T-F) bins selected by a combination of two weighting schemes, one using coherence, and the other using signal-to-noise ratio. When iterating, our method finds the significant SRP peaks for the dominant sources and eliminates the T-F bins contributed by these sources using inter-channel phase difference information. As a result, the remaining sources can be found in subsequent iterations among the remaining T-F bins. The proposed IR-SRP method is demonstrated using both simulated and measured experiment data.

A feature-based data association method for multiple acoustic source localization in a distributed microphone array.

Multisource localization using time difference of arrival (TDOA) is challenging because the correct combination of TDOA estimates across different microphone pairs, corresponding to the same source, is usually unknown, which is termed as the data association problem. Moreover, many existing multisource localization techniques are originally demonstrated in two dimensions, and their extensions to three dimensions (3D) are not straightforward and would lead to much higher computational complexity. In this paper, we propose an efficient, feature-based approach to tackle the data association problem and achieve multisource localization in 3D in a distributed microphone array. The features are generated by using interchannel phase difference (IPD) information, which indicates the number of times each frequency bin across all time frames has been assigned to sources. Based on such features, the data association problem is addressed by correlating most similar features across different microphone pairs, which is executed by solving a two-dimensional assignment problem successively. Thereafter, the locations of multiple sources can be obtained by imposing a single-source location estimator on the resulting TDOA combinations. The proposed approach is evaluated using both simulated data and real-world recordings.