Robust multi-source sound localization using temporal power fusion

In the past several years, many different algorithms have attempted to address the problem of robust multi-source time difference of arrival (TDOA) estimation, which is necessary for sound localization. Different approaches, including general cross correlation, multiple signal classification (MUSIC), and the maximum likelihood (ML) approach, have made different trade-offs between robustness and efficiency. A new approach presented here offers a much more efficient yet robust mechanism for TDOA estimation. This approach iteratively uses small sound signal segments to compute a local crosscorrelation based TDOA estimate. All of the different local estimates are combined to form the probability density function of the TDOA. Because the power of the secondary sources will be greater than the others for a certain set of the local signal segments, the TDOA corresponding to these sources will be associated with a peak in the TDOA probability density function. This way, the TDOAs of several different sources, along with their signal strength can be estimated. A real-time implementation of the proposed approach is used to show its improved accuracy and robustness. The system was consistently able to correctly localize sound sources with SNRs as low as 3 dB.