Iterative Diagonal Unloading Beamforming for Multiple Acoustic Sources Localization Using Compact Sensor Arrays

Abstract

Compact acoustic sensor arrays are nowadays popular devices for audio sensing due to minimum size and lightness characteristics that make it attractive for numerous applications such as human-computer interaction, acoustic scene analysis, teleconferencing systems, hearing aid, robotics, and bioacoustics. A compact array has a small inter-microphone distance that allows to reduce the spatial aliasing effect in the acoustic source localization problem, degrading however the spatial resolution. Thus, as a consequence, the capability of this class of sensors in the localization of multiple acoustic sources is rather limited. In this paper, we present an iterative diagonal unloading (IDU) beamforming suitable for the direction of arrivals (DOAs) estimation of concurrent multiple sources using compact microphone arrays. The method is based on the identification of the dominant signal, which corresponds to the largest peak in the pseudo-spectrum (i.e., the acoustic response map) computed by the DU beamformer, and on an iterative cancellation of the dominant signal from the covariance matrix to compute a new spatial pseudo-spectrum. All the pseudo-spectrum responses computed with the iterative procedure are summed to obtain a high resolution map of the acoustic scene. We analyze the DOAs estimation performance using compact uniform linear arrays (ULAs) and spherical microphone arrays (SMAs) with simulations and real acoustic data. The results show that the proposed IDU beamforming increases the spatial resolution allowing an effective localization of concurrent multiple sources.