Machine learning in acoustics: Theory and applications.

Area of Science:

• Acoustics
• Signal Processing
• Machine Learning
• Data Science

Background:

• Acoustic data are crucial across diverse scientific and engineering disciplines.
• Conventional acoustics and signal processing methods are often limited by data complexity.
• Machine learning (ML) provides data-driven approaches to uncover intricate patterns in acoustic data.

Purpose of the Study:

• To survey recent advancements in machine learning (ML) for acoustics.
• To highlight the transformative potential of ML, including deep learning, in acoustics.
• To showcase ML applications in key acoustics research areas.

Main Methods:

• Introduction to machine learning (ML) principles and statistical foundations.
• Review of ML techniques applied to acoustic data analysis.
• Exploration of ML's data-driven pattern detection capabilities.

Main Results:

• ML enables discovery of complex relationships in acoustic data with sufficient training.
• ML models can describe intricate acoustic phenomena like human speech and reverberation.
• Compelling results and significant promise demonstrated by ML in acoustics.

Conclusions:

• ML is rapidly advancing the field of acoustics.
• ML applications show significant promise in source localization, bioacoustics, and environmental sound analysis.
• The data-driven nature of ML offers a powerful paradigm shift for acoustic research.