This article presents a new method for localization of multiple concurrent speech sources that relies on simultaneous blind signal separation and direction of arrival (DOA) estimation, as well as a method to solve the intersection point selection problem that arises when locating multiple speech sources using multiple sensor arrays. The proposed method is based on a low complexity nonparametric blind signal separation method, making is suitable for real-time applications on embedded platforms. On top of reduced complexity in comparison to a previously presented method, the DOA estimation accuracy is also improved. Evaluation of the performance is done with both real recording and simulations, and a real-time prototype of the proposed method has been implemented on a DSP platform to evaluate the computational and the memory complexities in a real application.

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This article presents an analysis of the sensitivity of geometrical sensor errors in acoustic source localization using the well-established SRP-PHAT method.The array in this analysis is a uniform linear array and the intended source is human speech in the far field. Two major results are presented: inner-sensor geometrical errors in the linear array produce smaller localization errors than corresponding geometrical errors do in the two end-point sensors, and the localization error rises sharply for a total geometrical error exceeding the equivalence of the acoustic propagation distance of 2/3 of the sample time instance (approximately 3 cm at 8 kHz). The article also provides a mathematical and graphical explanation of the results.

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