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.

翻译：本文提出了一种融合物理方法与机器学习技术分析声信号的新型框架。针对该任务，我们开发了三种方法：用于推断频谱声学特性的贝叶斯推断方法、配备前向及反向物理损失函数的神经物理模型，以及作为基准的非线性最小二乘法。推断所得的传播系数可导出房间脉冲响应（RIR）量，该量可用于带不确定性的重定位。通过模拟数据实验验证了该框架的简洁性与高效性。