# Machine learning in acoustics: theory and applications

## TL;DR

This paper surveys recent advances in applying machine learning, especially deep learning, to acoustics, highlighting its data-driven approach and potential to model complex acoustic phenomena across various fields.

## Contribution

It provides a comprehensive overview of how machine learning techniques are transforming acoustics research and applications, covering four key areas with recent developments.

## Key findings

- ML enables complex acoustic modeling with large data sets
- Deep learning improves source localization accuracy
- ML shows promise across diverse acoustic fields

## Abstract

Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of techniques, which are often based in statistics, for automatically detecting and utilizing patterns in data. Relative to conventional acoustics and signal processing, ML is data-driven. Given sufficient training data, ML can discover complex relationships between features and desired labels or actions, or between features themselves. With large volumes of training data, ML can discover models describing complex acoustic phenomena such as human speech and reverberation. ML in acoustics is rapidly developing with compelling results and significant future promise. We first introduce ML, then highlight ML developments in four acoustics research areas: source localization in speech processing, source localization in ocean acoustics, bioacoustics, and environmental sounds in everyday scenes.