Machine Learning in Acoustics: A Review and Open-Source Repository
🤖 AI Summary
This paper addresses the growing need for automated pattern recognition and modeling in acoustic data analysis. It systematically reviews state-of-the-art machine learning techniques—including deep learning, generative models, and physics-informed neural networks—for acoustic classification, regression, and generation tasks. To bridge the gap between methodology and application, we introduce AcousticsML, an open-source library featuring reproducible Jupyter notebooks and standardized preprocessing pipelines, with unified data interfaces and model evaluation protocols. AcousticsML enables end-to-end acoustic signal analysis and physics-constrained modeling, substantially lowering the barrier to entry for interdisciplinary researchers. By integrating data-driven and mechanism-driven paradigms, the library fosters an open, collaborative research framework for acoustics. It provides scalable, verifiable technical support for real-world applications including environmental noise monitoring, speech enhancement, and structural health diagnostics. (149 words)
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Surveying ML advances in acoustics for scientific insights
Demonstrating Python-based ML techniques for acoustic data
Providing open-source examples to encourage reproducible approaches
Innovation
Methods, ideas, or system contributions that make the work stand out.
Python-based ML techniques for acoustics
Physics-informed neural networks for acoustics
AcousticsML GitHub repository for reproducible examples
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