Research Article Incident Signal Power Comparison for Localization of Concurrent Multiple Acoustic Sources Daniele Salvati 1 and Sergio Canazza 2 1 Department of Mathematics and Computer Science, University of Udine, 33100 Udine, Italy 2 Department of Information Engineering, University of Padova, 35131 Padova, Italy Correspondence should be addressed to Sergio Canazza; canazza@dei.unipd.it Received 5 August 2013; Accepted 2 January 2014; Published 20 February 2014 Academic Editors: S. Bourennane and J. Marot Copyright © 2014 D. Salvati and S. Canazza. his is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In this paper, a method to solve the localization of concurrent multiple acoustic sources in large open spaces is presented. he problem of the multisource localization in far-ield conditions is to correctly associate the direction of arrival (DOA) estimated by a network array system to the same source. he use of systems implementing a Bayesian ilter is a traditional approach to address the problem of localization in multisource acoustic scenario. However, in a real noisy open space the acoustic sources are oten discontinuous with numerous short-duration events and thus the iltering methods may have diiculty to track the multiple sources. Incident signal power comparison (ISPC) is proposed to compute DOAs association. ISPC is based on identifying the incident signal power (ISP) of the sources on a microphone array using beamforming methods and comparing the ISP between diferent arrays using spectral distance (SD) measurement techniques. his method solves the ambiguities, due to the presence of simultaneous sources, by identifying sounds through a minimization of an error criterion on SD measures of DOA combinations. he experimental results were conducted in an outdoor real noisy environment and the ISPC performance is reported using diferent beamforming techniques and SD functions. 1. Introduction he sensory capacity to analyze acoustic space is a very important function of an auditory system. he need for the development of an understanding of the sound environment has attracted many researchers over the past twenty years to build sensory systems that are capable of locating acoustic sources in space. Acoustic source localization (ASL) is an important task in a growing number of applications. Fields of application in which identiication of the location of acoustic sources is desired include audio surveillance, teleconferenc- ing systems, hands-free acquisition in car, system moni- toring, human-machine interaction, musical control inter- faces, videogames, virtual reality systems, voice recognition, fault analysis of machinery, autonomous robots, processors for digital hearing aids, high-quality recording, multiparty telecommunications, dictation systems, and acoustic scene analysis. he aim of an ASL system is to estimate the position of sound sources in space by analyzing the sound ield with a microphone array, a set of microphones arranged to capture the spatial information of sound. Several application areas that may potentially provide advantages in using the acoustic location have led to the development of many signal processing algorithms, which mostly consider the speciic acoustic environment, the signal properties, and the localization goal. ASL can be performed by two basic methods: indirect and direct. he indirect approach is used to estimate source positions by implementing the following two steps: in the irst one, a set of time diference of arrivals (TDOAs) are estimated using measurements across various combinations of microphones, and in the second one, when the position of the sensors and the speed of sound are known, the source positions can be estimated using geometric considerations and approximate estimators: closed-formed estimators based on a least squares solution [1–7] (for an overview on closed- form estimators, see [8]) and iterative maximum likelihood estimators [9–15]. he direct approach involves the search Hindawi Publishing Corporation e Scientific World Journal Volume 2014, Article ID 582397, 13 pages http://dx.doi.org/10.1155/2014/582397