Semantic Scholar Open Access 2025 5 sitasi

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

Ryan A. McCarthy You Zhang Samuel A. Verburg W. Jenkins P. Gerstoft

Abstrak

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.

Topik & Kata Kunci

Computer Science Engineering

Penulis (5)

Ryan A. McCarthy

You Zhang

Samuel A. Verburg

W. Jenkins

P. Gerstoft

Format Sitasi

APA MLA BibTeX

McCarthy, R.A., Zhang, Y., Verburg, S.A., Jenkins, W., Gerstoft, P. (2025). Machine Learning in Acoustics: A Review and Open-source Repository. https://doi.org/10.1038/s44384-025-00021-w

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Informasi Jurnal

Tahun Terbit: 2025
Bahasa: en
Total Sitasi: 5×
Sumber Database: Semantic Scholar
DOI: 10.1038/s44384-025-00021-w
Akses: Open Access ✓