Source localization and separation using Random Sample Consensus with phase cues

In this paper we present a system for localization and separation of multiple speech sources using phase cues. The novelty of this method is the use of Random Sample Consensus (RANSAC) approach to find consistency of interaural phase differences (IPDs) across the whole frequency range. This approach is inherently free from phase ambiguity problems and enables all phase data to contribute to localization. Another property of RANSAC is its robustness against outliers which enables multiple source localization with phase data contaminated by reverberation noise. Results of RANSAC based localization are fed into a mixture model to generate time-frequency binary masks for separation. System performance is compared against other well known methods and shows similar or improved performance in reverberant conditions.