Model-based Bayesian direction of arrival analysis for sound sources using a spherical microphone array

Abstract

In many room acoustics and noise control applications, it is often challenging to determine the directions of arrival (DoAs) of incoming sound sources. This work seeks to solve this problem reliably by beamforming, or spatially filtering, incoming sound data with a spherical microphone array via a probabilistic method. When estimating the DoA, the signal under consideration may contain one or multiple concurrent sound sources originating from different directions. This leads to a two-tiered challenge of first identifying the correct number of sources, followed by determining the directional information of each source. To this end, a probabilistic method of model-based Bayesian analysis is leveraged. This entails generating analytic models of the experimental data, individually defined by a specific number of sound sources and their locations in physical space, and evaluating each model to fit the measured data. Through this process, the number of sources is first estimated, and then the DoA information of those sources is extracted from the model that is the most concise to fit the experimental data. This paper will present the analytic models, the Bayesian formulation, and preliminary results to demonstrate the potential usefulness of this model-based Bayesian analysis for complex noise environments with potentially multiple concurrent sources.

A Model-Based Bayesian Framework for Pipeline Leakage Enumeration and Location Estimation

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“…Bayesian model selection is a probabilistic method of evaluating a finite set of models, given the observed data, and then seeking the model that best describes the data. The idea behind the model selection is to compare the posterior probability of a set of competing models (Escolano et al 2014;Landschoot and Xiang 2019;Sivia and Skilling 2006). This can be determined by the probability of the model, Mi, given the data, D, represented as P(Mi|D).…”

Acoustic source localization through the data fusion with multi-distributed microphone arrays

“…[19] developed a rescue robot by combining the speech recognition technology with an asymmetric pyramid microphone array. Spherical microphone array is widely used in acoustic and noise control systems [20] , room geometry estimation [21] , 3D audio equipment [22] and other fields due to its good symmetrical structure. However, the sound source localization based on a single microphone array has the disadvantages of narrow localization range, short localization distance and low localization accuracy.…”

Narrowband multi-source direction-of-arrival estimation in the spherical harmonic domain

“…denotes the sign operator. Note that such analytical solution may have no or only one valid answer if ≤ 0 in (37) or | cos(θ 1 )| > 1 in (33).…”

“…In SRP a beamformer such as Plane-Wave Decomposition (PWD)11,14,31 is applied to the raw signals. The recent works36,37 use model-based Bayesian analysis with beamforming to determine the number of sources and their DOAs. They generate and evaluate number of models, each defined by a specific number of sources and DOAs, to find the best fit to the data.…”