Machine Learning in Acoustics: A Review and Open-source Repository

• Published in: NPJ Acoustics Vol. 1; no. 1; p. 18
• Main Authors: McCarthy, Ryan A., Zhang, You, Verburg, Samuel A., Jenkins, William F., Gerstoft, Peter
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 09.09.2025; Nature Publishing Group
• Subjects: 639/166/987; 639/766/930/1032; 639/766/930/12; Acoustics; Algorithms; Audio data; Bioacoustics; Classification; Control; Datasets; Deep learning; Dynamical Systems; Earth sciences; Engineering Acoustics; High level languages; Machine learning; Materials Science; Neural networks; Noise Control; Physics; Physics and Astronomy; Programming languages; Python; Review; Ultrasound; Vibration
• ISSN: 3005-141X; 3005-141X
• DOI: 10.1038/s44384-025-00021-w

Acoustic data provide scientific and engineering insights in fields ranging from bioacoustics and communications to ocean and earth sciences. In this review, we survey recent advances and the transformative potential of machine learning (ML) in acoustics including deep learning (DL). Using the Python high-level programming language, we demonstrate a broad collection of ML techniques to detect and find patterns for classification, regression, and generation in acoustics data automatically. We have ML examples including acoustic data classification, generative modeling for spatial audio, and physics-informed neural networks. This work includes AcousticsML , a set of practical Jupyter notebook examples on GitHub demonstrating ML benefits and encouraging researchers and practitioners to apply reproducible data-driven approaches to acoustic challenges.