DocumentCode :
1805558
Title :
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
Link To Document :
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