Classification of Birds using FFT and Artificial Neural Networks

dc.contributor.author	Abewardana, Anuradha
dc.contributor.author	Sonnadara, D.U.J.
dc.date.accessioned	2013-01-22T05:34:09Z
dc.date.available	2013-01-22T05:34:09Z
dc.date.issued	2012
dc.identifier.citation	Proceedings of the Technical Sessions, Institute of Physics Sri Lanka, 28 (2012) 100-105	en_US
dc.identifier.uri	http://archive.cmb.ac.lk:8080/xmlui/handle/70130/3824
dc.description.abstract	The use of feed-forward artificial neural network to categorize a selected set of Sri Lankan bird species based on their vocalization is presented. The inputs to the neural network were frequencies of bird vocalizations where each vocalization was characterized by a frequency range. Out of the selected birds, only two birds showed peak frequency values below 1,000 Hz. The Sri Lanka Scaly Thrush has the maximum average peak frequency of 7,761 Hz and the Green Billed Coucal has the lowest of 334 Hz. The preliminary results show that the artificial neural network which was trained to classify individual birds based on their frequency features had an accuracy of greater than 90% for several bird types
dc.language.iso	en	en_US
dc.subject	Neural networks	en_US
dc.subject	Fast Fourier Transform	en_US
dc.title	Classification of Birds using FFT and Artificial Neural Networks	en_US
dc.type	Research paper	en_US