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Related Experiment Video

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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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Beyond traditional wind farm noise characterisation using transfer learning.

Phuc D Nguyen¹, Kristy L Hansen¹, Bastien Lechat²

¹College of Science and Engineering, Flinders University, Adelaide, South Australia 5042, Australia.

JASA Express Letters

|September 26, 2022

Summary

This summary is machine-generated.

This study introduces deep acoustic features for wind farm noise (WFN) assessment. This AI-driven approach offers superior spatial and temporal noise representation compared to traditional methods.

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Area of Science:

Acoustics
Artificial Intelligence
Environmental Science

Background:

Wind farm noise (WFN) assessment traditionally relies on spectral analysis and overall noise descriptors.
Existing methods may not fully capture the complex spatial and temporal characteristics of WFN.

Purpose of the Study:

To propose and evaluate a novel approach for characterizing and assessing wind farm noise.
To leverage deep learning for extracting advanced acoustic features for WFN analysis.

Main Methods:

Extraction of acoustic features between 125 and 7500 Hz using a pretrained deep learning model, termed deep acoustic features.
Analysis of measured data from various locations to link deep acoustic features with noise characteristics.

Main Results:

Deep acoustic features correlate with meaningful characteristics of wind farm noise.
This approach provides an improved spatial and temporal representation of WFN.
Demonstrated superiority over traditional spectral analysis and overall noise descriptors.

Conclusions:

The proposed deep acoustic feature approach shows promise for WFN assessment.
This method could form the basis for a future, enhanced framework for wind farm noise evaluation.