| Autor: |
Mirzaei, Golrokh, Majid, Mohammad Wadood, Ross, Jeremy, Jamali, Mohsin M., Gorsevski, Peter V., Frizado, Joseph P., Bingman, Verner P. |
| Zdroj: |
2012 IEEE International Conference on Electro/Information Technology; 1/ 1/2012, p1-4, 4p |
| Abstrakt: |
There are reports that large number of bat fatalities occur near wind turbines. Acoustic characteristics can be employed for bat call recognition to better understand the effects of turbines on different bat species. Acoustic features of bat echolocation calls are extracted based on three different techniques: Short Time Fourier Transform (STFT), Mel Frequency Cepstrum Coefficient (MFCC) and Discrete Wavelet Transform (DWT). These features are fed into an Evolutionary Neural Network (ENN) for their classification at the species level using acoustic features. Results from different feature extraction techniques are compared based on classification accuracy. The technique can identify bats and will contribute towards developing mitigation procedures for reducing bat fatalities. [ABSTRACT FROM PUBLISHER] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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