In this paper we present an efficient method to perform acoustic source localization and tracking using a distributed network of microphones. In this scenario, there is a trade-off between the localization performance and the expense of resources: in fact, a minimization of the localization error would require to use as many sensors as possible; at the same time, as the number of microphones increases, the cost of the network inevitably tends to grow, while in practical applications only a limited amount of resources is available. Therefore, at each time instant only a subset of the sensors should be enabled in order to meet the cost constraints. We propose a heuristic method for the optimal selection of this subset of microphones, using as distortion metrics the Cramer-Rao Lower Bound (CRLB) and as cost function the total distance between the selected sensors. The heuristic approach has been compared to an optimal algorithm, which searches the best sensor configuration among the full set of microphones, while satisfying the cost constraint. The proposed heuristic algorithm yields similar performance w.r.t. the full-search procedure, but at a much less computational cost. We show that this method can be used effectively in an acoustic source tracking application.

Video-based surveillance systems may benefit from the integration with microphone arrays for the localization of sound events. Applying the sound localization techniques to the surveillance of large areas requires addressing some open issues, such as the non uniform resolution of the microphones-based localization systems. This paper presents a new method for tracking moving sound events based on an Hidden Markov Model (HMM), which exploits a priori information derived from medium and longterm observations of the monitored area. The results obtained with simulated trajectories show that the HMMbased tracker is able to significantly reduce the localization error. Applications can be found in surveillance systems for large areas, such as square, streets, or parking lots, where it is of interest the monitoring of moving vehicles and people. 1.

Abstract — This paper addresses the TDOA extraction problem for localizing multiple sources in noisy and reverberant environments with emphasis on speech excitation. TDOAs are estimated by performing blind adaptive MIMO system identification using a gradient-based BSS variant of the TRINICON framework. We present a novel method to improve the TDOA estimation for signals with lowpass-like spectral characteristics such as speech, for which the standard approach achieves only imperfect system identification. To this end, we propose to combine the BSS algorithm with decorrelation filters, thereby achieving a greatly improved wideband identification of the acoustical system. The approach is verified in a number of scenarios, where it provides more accurate TDOA estimates for the speaker localization at a negligible additional computational cost. I.

A real-time demonstrator for the 2D localization of two sound sources using two microphone pairs is presented and evaluated. The scheme relies on Blind Source Separation (BSS) to adaptively identify the acoustical MIMO system, hence allowing the estimation of relative time delays for each source and each dimension. Extending our previously presented work [1], a mechanism to solve a pairing problem occuring in the multidimensional localization of several sources is described. It exploits the inherent signal extraction abilities of BSS. Experimental evaluations with large microphone apertures show that the demonstrator can accurately localize two speech sources in a 2D space, with a precision better than one degree.

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