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Localization of multiple acoustic sources in a non-ideal environment has a number of difficulties, among which are accurate acoustic feature estimation for multiple sources and association uncertainty between measurements and their corresponding sources. This paper focuses more on the latter and proposes an algorithm based on a multiple-hypothesis framework for both a measurement model and a measurement association model to localize multiple sources. A conditional data likelihood model based on a measurement hypothesis is proposed and implemented using particles. Simulation results demonstrate that the proposed algorithm is capable of localizing the positions of multiple sources with a small number of microphones without any prior knowledge when the amount of reverberation is moderate.

The lack of a common clock reference is a fundamental problem when dealing with audio streams originating from or heading to different distributed sound capture or playback devices. When implementing multichannel signal processing algorithms for such kind of audio streams it is necessary to account for the unavoidable mismatches between the actual sampling rates. There are some approaches that can help to correct these mismatches, but important problems remain to be solved, among them the accurate estimation of the mismatch factors, and achieving both accuracy and computational efficiency in their correction. In this paper we present an empirical study on the performance of blind source separation and acoustic echo cancellation algorithms in this scenario. We also analyze the degradation in performance when using an approximate but efficient method to correct the rate mismatches.