# Machine learning in acoustics: theory and applications

arXiv: https://arxiv.org/abs/1905.04418 · HTML: https://mlsanity.com/paper/1905.04418/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2019.

## Built on by (most cited first, 30 of 36)

- [A Survey of Sound Source Localization with Deep Learning Methods](https://mlsanity.com/paper/2109.03465/) (2021)
- [Improving aircraft performance using machine learning: a review](https://mlsanity.com/paper/2210.11481/) (2022)
- [Sound Localization by Self-Supervised Time Delay Estimation](https://mlsanity.com/paper/2204.12489/) (2022)
- [Implicit neural representation with physics-informed neural networks for the reconstruction of the early part of room impulse responses](https://mlsanity.com/paper/2306.11509/) (2023)
- [Reconstruction of Sound Field through Diffusion Models](https://mlsanity.com/paper/2312.08821/) (2023)
- [A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic](https://mlsanity.com/paper/2204.06362/) (2022)
- [The Galerkin method beats Graph-Based Approaches for Spectral Algorithms](https://mlsanity.com/paper/2306.00742/) (2023)
- [Semi-supervised source localization in reverberant environments with deep generative modeling](https://mlsanity.com/paper/2101.10636/) (2021)
- [Generative adversarial networks with physical sound field priors](https://mlsanity.com/paper/2308.00426/) (2023)
- [Learning to Predict Structural Vibrations](https://mlsanity.com/paper/2310.05469/) (2023)
- [A Data-Driven Approach to Violin Making](https://mlsanity.com/paper/2102.04254/) (2021)
- [Echo-Reconstruction: Audio-Augmented 3D Scene Reconstruction](https://mlsanity.com/paper/2110.02405/) (2021)
- [ElectrodeNet -- A Deep Learning Based Sound Coding Strategy for Cochlear Implants](https://mlsanity.com/paper/2305.16753/) (2023)
- [ISR: Invertible Symbolic Regression](https://mlsanity.com/paper/2405.06848/) (2024)
- [A data-driven two-microphone method for in-situ sound absorption measurements](https://mlsanity.com/paper/2502.04143/) (2025)
- [Acoustic Structure Inverse Design and Optimization Using Deep Learning](https://mlsanity.com/paper/2102.02063/) (2021)
- [Audio scene monitoring using redundant ad-hoc microphone array networks](https://mlsanity.com/paper/2103.01830/) (2021)
- [Near field Acoustic Holography on arbitrary shapes using Convolutional Neural Network](https://mlsanity.com/paper/2103.16935/) (2021)
- [SofaMyRoom: a fast and multiplatform "shoebox" room simulator for binaural room impulse response dataset generation](https://mlsanity.com/paper/2106.12992/) (2021)
- [A Machine-learning Framework for Acoustic Design Assessment in Early Design Stages](https://mlsanity.com/paper/2109.06459/) (2021)
- [Direct Localization in Underwater Acoustics via Convolutional Neural Networks: A Data-Driven Approach](https://mlsanity.com/paper/2207.10222/) (2022)
- [Room Transfer Function Reconstruction Using Complex-valued Neural Networks and Irregularly Distributed Microphones](https://mlsanity.com/paper/2402.04866/) (2024)
- [Automated Bioacoustic Monitoring for South African Bird Species on Unlabeled Data](https://mlsanity.com/paper/2406.13579/) (2024)
- [A Review on Sound Source Localization in Robotics: Focusing on Deep Learning Methods](https://mlsanity.com/paper/2507.01143/) (2025)
- [Expert Decision Support System for aeroacoustic source type identification using clustering](https://mlsanity.com/paper/2103.00255/) (2021)
- [Supplementation of deep neural networks with simplified physics-based features to increase model prediction accuracy](https://mlsanity.com/paper/2204.06764/) (2022)
- [An experiment on an automated literature survey of data-driven speech enhancement methods](https://mlsanity.com/paper/2310.06260/) (2023)
- [ML-ASPA: A Contemplation of Machine Learning-based Acoustic Signal Processing Analysis for Sounds, & Strains Emerging Technology](https://mlsanity.com/paper/2402.10005/) (2023)
- [VINP: Variational Bayesian Inference with Neural Speech Prior for Joint ASR-Effective Speech Dereverberation and Blind RIR Identification](https://mlsanity.com/paper/2502.07205/) (2025)
- [Prediction of acoustic field in 1-D uniform duct with varying mean flow and temperature using neural networks](https://mlsanity.com/paper/2507.22370/) (2025)

## Abstract (arXiv)

Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of techniques, which are often based in statistics, for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in four acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, and environmental sounds in everyday scenes.