Synthesis of ICA-based methods for localization of multiple broadband sound sources

In this paper, minimization of the statistical dependence is exploited for acoustic source localization purposes. Originally developed for the separation of signal mixtures, we show that Independent Component Analysis (ICA) can also be successfully applied to localize multiple simultaneously active sound sources, with possibly less sensors than sources. First, the recently proposed Averaged Directivity Pattern (ADP) and State Coherence Transform (SCT) methods are reviewed. Similarities and differences between both approaches are underlined and analyzed, leading to a new method merging elements from both concepts, which we call the Modified ADP (MADP). Since the investigated methods do not suffer from the permutation ambiguity, they can be applied in combination with any narrowband or broadband ICA algorithm, without the need to solve the still challenging permutation issue. Experimental results are presented for speech sources in a reverberant environment.