Acoustic monitoring techniques for avian detection and classification

Author

Mirzaei, Golrokh ; Wadood Majid, Mohammad ; Bastas, S. ; Ross, James ; Jamali, Mohsin M. ; Gorsevski, Peter V. ; Frizado, Joseph ; Bingman, Verner P.

Author_Institution

Dept. of Electr. & Comp Sci., Univ. of Toledo, Toledo, OH, USA

fYear

2012

fDate

4-7 Nov. 2012

Firstpage

1835

Lastpage

1838

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.

Keywords

acoustic measurement; biology computing; computerised monitoring; data analysis; evolutionary computation; feature extraction; neural nets; pattern classification; wind power plants; wind turbines; ENN; Ohio; SIFS; acoustic monitoring techniques; avian classification; avian detection; bat mortality; bird mortality; data analysis; evolutionary neural network; fall migration periods; feature extraction; spectrogram-based image frequency statistics; wind farm development; wind turbines;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers (ASILOMAR), 2012 Conference Record of the Forty Sixth Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

978-1-4673-5050-1

Type

conf

DOI

10.1109/ACSSC.2012.6489353

Filename

6489353