WIDEBAND MICROPHONE ARRAY SYSTEM FOR SOUND LOCALIZATION AND SPECIES IDENTIFICATION IN WIND FARM ENVIRONMENTS

Wind energy facilities provide a sustainable power source but pose a significant threat to birds and bats that traverse these areas. To mitigate this risk, an acoustic monitoring system was designed to track and classify volant species. The system consists of an array of wideband microphones that enable time difference of arrival (TDOA) based 3D source localization using the generalized cross-correlation phase transform (GCC-PHAT) algorithm, accurate to within 1 degree in isolated environments. Furthermore, the microphones utilize a convolutional neural network (CNN) for species identification. The collected data maps migration patterns in the areas surrounding wind energy facilities, providing wind energy companies with actionable insights to adjust operations and reduce harm to wildlife.

Authors

Publication Details

Journal
UA Campus Repository (The University of Arizona)
Published
2025-10-01
Primary Topic
Animal Vocal Communication and Behavior
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

WIDEBAND MICROPHONE ARRAY SYSTEM FOR SOUND LOCALIZATION AND SPECIES IDENTIFICATION IN WIND FARM ENVIRONMENTS

Yogananda Isukapalli, Max Stewart, Morgan Parslow, Derek Dela Cruz et al.
UA Campus Repository (The University of Arizona)
Animal Vocal Communication and Behavior
article

WIDEBAND MICROPHONE ARRAY SYSTEM FOR SOUND LOCALIZATION AND SPECIES IDENTIFICATION IN WIND FARM ENVIRONMENTS

Yogananda Isukapalli, Max Stewart, Morgan Parslow, Derek Dela Cruz, Lizette Crooks, Tien Dung Nguyen
article en

Abstract

Wind energy facilities provide a sustainable power source but pose a significant threat to birds and bats that traverse these areas. To mitigate this risk, an acoustic monitoring system was designed to track and classify volant species. The system consists of an array of wideband microphones that enable time difference of arrival (TDOA) based 3D source localization using the generalized cross-correlation phase transform (GCC-PHAT) algorithm, accurate to within 1 degree in isolated environments. Furthermore, the microphones utilize a convolutional neural network (CNN) for species identification. The collected data maps migration patterns in the areas surrounding wind energy facilities, providing wind energy companies with actionable insights to adjust operations and reduce harm to wildlife.

UA Campus Repository (The University of Arizona)
Affordable and clean energy
Openalex Percentile: Top 24%
Animal Vocal Communication and Behavior
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.