Beyond traditional wind farm noise characterisation using transfer learning-Reference-Cited by-同舟云学术

Beyond traditional wind farm noise characterisation using transfer learning

Published:2022-05 Issue:5 Volume:2 Page:052801
ISSN:2691-1191
Container-title:JASA Express Letters
language:en
Short-container-title:JASA Express Letters

Author:

Nguyen Phuc D.¹^ORCID,Hansen Kristy L.¹^ORCID,Lechat Bastien²^ORCID,Zajamsek Branko¹^ORCID,Hansen Colin³^ORCID,Catcheside Peter²^ORCID

Affiliation:

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

2. Adelaide Institute for Sleep Health, Flinders University, Adelaide, South Australia 5042, Australia

3. School of Mechanical Engineering, University of Adelaide, Adelaide, South Australia 5005, Australia, , , , ,

Abstract

This study proposes an approach for the characterisation and assessment of wind farm noise (WFN), which is based on extraction of acoustic features between 125 and 7500 Hz from a pretrained deep learning model (referred to as deep acoustic features). Using data measured at a variety of locations, this study shows that deep acoustic features can be linked to meaningful characteristics of the noise. This study finds that deep acoustic features can reveal an improved spatial and temporal representation of WFN compared to what is revealed using traditional spectral analysis and overall noise descriptors. These results showed that this approach is promising, and thus it could provide the basis for an improved framework for WFN assessment in the future.

Funder

Australian Research Council

National Health and Medical Research Council

Publisher

Acoustical Society of America (ASA)

Subject

Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics

Link

https://asa.scitation.org/doi/pdf/10.1121/10.0010494

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1. Beyond traditional wind farm noise characterisation using transfer learning;JASA Express Letters;2022-05