# Bayesian inference and neural estimation of acoustic wave propagation

arXiv: https://arxiv.org/abs/2305.17749 · HTML: https://mlsanity.com/paper/2305.17749/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 52 references that are in the map.
- First posted on arXiv 28 May 2023.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [An overview of gradient descent optimization algorithms](https://mlsanity.com/paper/1609.04747/) (2016)
- [SoundSpaces 2.0: A Simulation Platform for Visual-Acoustic Learning](https://mlsanity.com/paper/2206.08312/) (2022)
- [Learning Neural Acoustic Fields](https://mlsanity.com/paper/2204.00628/) (2022)
- [On Markov chain Monte Carlo methods for tall data](https://mlsanity.com/paper/1505.02827/) (2015)
- [Few-Shot Audio-Visual Learning of Environment Acoustics](https://mlsanity.com/paper/2206.04006/) (2022)
- [Visual Camera Re-Localization Using Graph Neural Networks and Relative Pose Supervision](https://mlsanity.com/paper/2104.02538/) (2021)
- [FAST-RIR: Fast neural diffuse room impulse response generator](https://mlsanity.com/paper/2110.04057/) (2021)
- [Deep Impulse Responses: Estimating and Parameterizing Filters with Deep Networks](https://mlsanity.com/paper/2202.03416/) (2022)
- [On the Computational Complexity of High-Dimensional Bayesian Variable Selection](https://mlsanity.com/paper/1505.07925/) (2015)
- [Foundations of Sequence-to-Sequence Modeling for Time Series](https://mlsanity.com/paper/1805.03714/) (2018)
- [TS-RIR: Translated synthetic room impulse responses for speech augmentation](https://mlsanity.com/paper/2103.16804/) (2021)
- [Improving Reverberant Speech Training Using Diffuse Acoustic Simulation](https://mlsanity.com/paper/1907.03988/) (2019)
- [SoundDet: Polyphonic Moving Sound Event Detection and Localization from Raw Waveform](https://mlsanity.com/paper/2106.06969/) (2021)
- [Efficient Synthesis of Room Acoustics via Scattering Delay Networks](https://mlsanity.com/paper/1502.05751/) (2015)

## Built on by (most cited first, 1 of 1)

- [BiJEPA: Bi-directional Joint Embedding Predictive Architecture for Symmetric Representation Learning](https://mlsanity.com/paper/2603.00049/) (2026)

## Abstract (arXiv)

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.