Bird song identification using artificial neural networks and statistical analysis

Author

McIlraith, Alex L. ; Card, Howard C.

Author_Institution

Dept. of Electr. & Comput. Eng., Manitoba Univ., Winnipeg, Man., Canada

Volume

1

fYear

1997

fDate

25-28 May 1997

Firstpage

63

Abstract

A system for automatically identifying six bird species by their songs was implemented. Pre-processing of sampled songs extracted temporal measurements of periods of sound and silence within songs. Power spectral densities were used to extract spectral information. Statistical methods were used to reduce data dimensionality and for identification tasks. An artificial neural network was also used for identification. Quadratic discriminant analysis achieved a 93%, and a backpropagation neural network 82% overall accuracy

Keywords

acoustic signal processing; backpropagation; biocommunications; biology computing; neural nets; pattern recognition; statistical analysis; artificial neural networks; backpropagation neural network; bird song identification; bird species; data dimensionality; identification tasks; power spectral densities; quadratic discriminant analysis; sampled song preprocessing; spectral information; statistical analysis; temporal measurement extraction; Acoustical engineering; Animals; Artificial neural networks; Birds; Data mining; Evolution (biology); Feature extraction; Humans; Speech analysis; Statistical analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical and Computer Engineering, 1997. Engineering Innovation: Voyage of Discovery. IEEE 1997 Canadian Conference on

Conference_Location

St. Johns, Nfld.

ISSN

0840-7789

Print_ISBN

0-7803-3716-6

Type

conf

DOI

10.1109/CCECE.1997.614790

Filename

614790