Machine Learning in Acoustics: A Review and Open-Source Repository
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911041817935872 |
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| author | McCarthy, Ryan A. Zhang, You Verburg, Samuel A. Jenkins, William F. Gerstoft, Peter |
| author_facet | McCarthy, Ryan A. Zhang, You Verburg, Samuel A. Jenkins, William F. Gerstoft, Peter |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04419 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning in Acoustics: A Review and Open-Source Repository McCarthy, Ryan A. Zhang, You Verburg, Samuel A. Jenkins, William F. Gerstoft, Peter Sound Audio and Speech Processing Signal Processing 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. |
| title | Machine Learning in Acoustics: A Review and Open-Source Repository |
| topic | Sound Audio and Speech Processing Signal Processing |
| url | https://arxiv.org/abs/2507.04419 |