Acoustic scene analysis by multiperspective sector-based sound field decomposition

Abstract

In this thesis, the performance of high-order sound field analysis by spatial filtering is evaluated in the context of 3D source localization. First, various factors in ‘sector- based’ processing—including analysis order, the presence of interfering noise, and source direction—are assessed using two key sound field indicators derived from intensimetric analysis: sound field diffuseness estimation and source direction-of- arrival (DoA) estimation. This carries forward to the evaluation of a 3D source localization technique which utilizes concurrent analyses from multiple receivers. The evaluation is carried out by simulation of ideal spherical harmonic receiver signals of sound fields comprised of fundamental components—a mixture of plane wave sound sources and an isotropic diffuse field—which adhere to an underlying plane wave model that has previously been characterized analytically. This model is then challenged with two scenarios: the presence of an interfering sound source, and an anisotropic, partially-correlated reverberant field. The high-order, sector- based approach to source localization is shown to be a consistent improvement upon the (first-order) method without spatial filtering, operating primarily through the principle of increasing the direct-to-diffuse (or signal-to-noise) energy ratio. However, numerous considerations must be taken if robust localization is to be performed over a large spatial extent. These include sector orientation, the arrangement and number of receivers, and estimation filtering and culling. Informed by the system performance under the tested conditions as well as the analytical model describing the sound field under the action of spatial filtering, optimization techniques are proposed, tested, and found to be successful under specified constraints. These include employing the diffuseness metric for weighted-DoA estimation in the localization task, an iterative approach localization using the estimated source distance to weight the contribution of DoA estimates, and a spatial sweep technique which applies a diffuseness constraint for DoA estimation culling which moves toward multisource localization.