Bayesian inference and neural estimation of acoustic wave propagation

• URL: http://arxiv.org/abs/2305.17749v1
• Date: Sun, 28 May 2023 15:14:46 GMT
• Title: Bayesian inference and neural estimation of acoustic wave propagation
• Authors: Yongchao Huang, Yuhang He, Hong Ge
• Abstract summary: We introduce a novel framework which combines physics and machine learning methods to analyse acoustic signals. Three methods are developed for this task: a Bayesian inference approach for inferring the spectral acoustics characteristics, a neural-physical model which equips a neural network with forward and backward physical losses, and the non-linear least squares approach which serves as benchmark. The simplicity and efficiency of this framework is empirically validated on simulated data.
• Score: 10.980762871305279
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this work, we introduce a novel framework which combines physics and machine learning methods to analyse acoustic signals. Three methods are developed for this task: a Bayesian inference approach for inferring the spectral acoustics characteristics, a neural-physical model which equips a neural network with forward and backward physical losses, and the non-linear least squares approach which serves as benchmark. The inferred propagation coefficient leads to the room impulse response (RIR) quantity which can be used for relocalisation with uncertainty. The simplicity and efficiency of this framework is empirically validated on simulated data.

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