On the angular error of intensity vector based direction of arrival estimation in reverberant sound fields

Abstract

An acoustic vector sensor provides measurements of both the pressure and particle velocity of a sound field in which it is placed. These measurements are vectorial in nature and can be used for the purpose of source localization. A straightforward approach towards determining the direction of arrival (DOA) utilizes the acoustic intensity vector, which is the product of pressure and particle velocity. The accuracy of an intensity vector based DOA estimator in the presence of noise has been analyzed previously. In this paper, the effects of reverberation upon the accuracy of such a DOA estimator are examined. It is shown that particular realizations of reverberation differ from an ideal isotropically diffuse field, and induce an estimation bias which is dependent upon the room impulse responses (RIRs). The limited knowledge available pertaining the RIRs is expressed statistically by employing the diffuse qualities of reverberation to extend Polack's statistical RIR model. Expressions for evaluating the typical bias magnitude as well as its probability distribution are derived.

3D DOA estimation of multiple sound sources based on spatially constrained beamforming driven by intensity vectors

“…Due to its tolerable latency, the intensity vector is an ideal candidate for real-time DOA estimation and has been previously employed in time-frequency domain spatial sound processing [6]. Its performance has been examined in reverberant environments [7] and a pseudo intensity vector has been formulated in the spherical harmonic domain [2,8].…”

A distributed particle filter for acoustic source tracking using an acoustic vector sensor network

“…The AVS manifold is independent of source signal's frequency, which enhances its utility in wideband acoustic signal processing applications [1], including acoustic source detection [2], localization [1] and tracking [3], [4] in underwater, battlefield, and room acoustic environments [5], [6]. More recent AVS applications include speech enhancement for hands free communication, sound source localization in air, and detection of seismic activities.…”

Reverberant speech separation with probabilistic time–frequency masking for B-format recordings

“…It is noticed in (Levin et al, 2010) that the intensity-based DOA estimation method produces biased results under reverberant environment. To address this problem, a new approach based on thresholding is proposed next.…”

“…Firstly, the separation performance of these two methods is strongly dependent on the accuracy of the DOA information, however, as demonstrated in (Levin et al, 2010), the intensity based DOA estimation, which is used in these two methods, produces biased results under reverberant environment, and the angular error becomes larger with the increase of the reverberation level. Secondly, the separation performance of the two algorithms is dependent on the accuracy of the estimation of mean directions, which are identified by the histogram peaks.…”