Acoustic Monitoring Techniques for Avian Detection and Classification

Conference Paper

Title: Acoustic Monitoring Techniques for Avian Detection and Classification

Author:

Mirzaei, G.; Majid, M.; Ross, J.; Jamali, M.; Gorsevski, P.; Frizado, J.; Bingman, V.; Bastas, S.

Publication Date:

November 4, 2012

Event Name:

46th Asilomar Conference on Signals, Systems and Computers (ASILOMAR 2012)

Event Location:

Pacific Grove, CA, USA

Pages:

1835-1838

Publisher:

IEEE

Affiliation

Bowling Green State University, University of Toledo

Technology:

Wind Energy, Land-Based Wind

Stressor:

Collision

Receptor:

Birds

Document Access

Website:

External Link

Citation

Mirzaei, G.; Majid, M.; Ross, J.; Jamali, M.; Gorsevski, P.; Frizado, J.; Bingman, V.; Bastas, S. (2012). Acoustic Monitoring Techniques for Avian Detection and Classification. Paper presented at 46th Asilomar Conference on Signals, Systems and Computers (ASILOMAR 2012), Pacific Grove, CA, USA.

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Abstract

There are many reports of bird and bat mortality in vicinity of wind turbines [1]. It is important to quantify numbers and species of birds and bats in a given area which is targeted for wind farm development. It is also necessary to assess the behavior of birds and bats in wind farm areas. Acoustic monitoring techniques have been developed in this work for monitoring of birds and bats. Spectrogram-based Image Frequency Statistics (SIFS) is used for feature extraction and Evolutionary Neural Network (ENN) is used for classification purposes. Data was collected near Lake Erie in Ohio during 2011 spring and fall migration periods. Data analysis was performed in accordance to needs of wildlife biologists.