Introduction to the special issue on machine learning in acoustics.

Area of Science:

• Acoustics
• Machine Learning
• Data Science

Background:

• Machine learning (ML) has gained significant traction in acoustics over the past decade.
• ML's applicability spans all sub-disciplines within acoustics.
• Acoustic data analysis benefits from ML's ability to uncover statistically derived insights.

Purpose of the Study:

• To highlight the broad applicability and transformative potential of ML in acoustics.
• To showcase the diverse range of research employing ML techniques in acoustic data analysis.
• To illustrate how ML extracts novel information from acoustic observations.

Main Methods:

• Review of 61 papers within a special issue focused on ML in acoustics.
• Categorization of ML applications across various acoustic domains.
• Analysis of the types of insights derived from ML-enhanced acoustic data.

Main Results:

• ML is a versatile tool applicable to all areas of acoustics.
• ML enables the extraction of statistically significant new information from acoustic data.
• The reviewed papers demonstrate a wide array of ML applications in acoustics.

Conclusions:

• Machine learning is revolutionizing acoustic research and applications.
• The integration of ML provides deeper scientific and engineering understanding from acoustic data.
• Future acoustic research will likely see increased adoption and innovation in ML methodologies.