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.
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.
In both entertainment and professional applications, conventionally produced stereo or multi-channel audio content is frequently delivered over headphones or earbuds. Use cases involving object-based binaural audio rendering include recently developed immersive multi-channel audio distribution formats, along with the accelerating deployment of virtual or augmented reality applications and head-mounted displays. The appreciation of these listening experiences by end users may be compromised by an unnatural perception of the localization of frontal audio objects: commonly heard near or inside the listener’s head even when their specified position is distant. This artifact may persist despite the provision of perceptual cues that have been known to partially mitigate it, including artificial acoustic reflections or reverberation, head-tracking, individualized HRTF processing, or reinforcing visual information. In this paper, we review previously reported methods for binaural audio externalization processing, and generalize a recently proposed approach to address object-based audio rendering.
A common task in acoustical applications is the determination of directions of arrival (DoAs) of sound at a receiver. This work aims to address this problem in situations involving potentially multiple simultaneous sound sources by means of a two-level framework of Bayesian inference. This process involves first estimating the number of sound sources present, followed by estimating their directional information, based on sound data collected with a spherical microphone array. Analytical models are formulated using spherical harmonic beamforming techniques, which are incorporated into the Bayesian analysis as part of the prior information. The experimental data are also incorporated to update the information available prior to analysis. All necessary prior information is assigned based on the principle of maximum entropy. Through this technique, the number of sources is first estimated, and then, the DoA information of those sources is extracted from the most concise model that adequately fits the experimental data. This paper presents the Bayesian formulation and analysis results to demonstrate the potential usefulness of model-based Bayesian inference for determining DoAs in complex noise environments with potentially multiple concurrent sources.
In many room acoustics and noise control applications, it is often challenging to identify the directions of arrivals (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 being 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.
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