Abstract
This work applies two levels of inference within a Bayesian framework to accomplish estimation of the directions of arrivals (DoAs) of sound sources. The sensing modality is a spherical microphone array based on spherical harmonics beamforming. When estimating the DoA, the acoustic signals may potentially contain one or multiple simultaneous sources. Using two levels of Bayesian inference, this work begins by estimating the correct number of sources via the higher level of inference, Bayesian model selection. It is followed by estimating the directional information of each source via the lower level of inference, Bayesian parameter estimation. This work formulates signal models using spherical harmonic beamforming that encodes the prior information on the sensor arrays in the form of analytical models with an unknown number of sound sources, and their locations. Available information on differences between the model and the sound signals as well as prior information on directions of arrivals are incorporated based on the principle of the maximum entropy. Two and three simultaneous sound sources have been experimentally tested without prior information on the number of sources. Bayesian inference provides unambiguous estimation on correct numbers of sources followed by the DoA estimations for each individual sound sources. This paper presents the Bayesian formulation, and analysis results to demonstrate the potential usefulness of the model-based Bayesian inference for complex acoustic environments with potentially multiple simultaneous sources.
Highlights
This paper offers a solution to the problem of localizing multiple simultaneous acoustic sources in acoustic environments through a model-based probabilistic approach
This research demonstrates that the number of sound sources as well as the directions in which they arrive can be estimated given a set of sound signals recorded on a spherical microphone array [1,2]
This paper demonstrates that the model-based Bayesian probabilistic approach can be applied to spatial sound field analysis with a set of sound signals recorded on a spherical microphone array
Summary
This paper offers a solution to the problem of localizing multiple simultaneous acoustic sources in acoustic environments through a model-based probabilistic approach. The estimation requires a process known as spherical beamforming, or the spatial filtering of a sound signal using spherical harmonics theory [3] This is combined with probabilistic inference using Bayesian model selection and parameter estimation [4,5]. This work applies Bayesian model selection to the DoA estimation tasks when the number of sound sources is unknown prior to the analysis. This paper demonstrates that the model-based Bayesian probabilistic approach can be applied to spatial sound field analysis with a set of sound signals recorded on a spherical microphone array. This approach estimates the number of sound sources as well as the directions of arrivals via two levels of Bayesian inference.
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