Instituto Tecnológico y de Estudios Superiores de Monterrey
Campus Estado de México
and the University of California, Los Angeles

 

Data Mining Applied to Acoustic Bird Species Recognition
Erika Vilches, Ivan A. Escobar, Edgar E. Vallejo, Charles E. Taylor
A00461595@itesm.mx , iescobar@itesm.mx , vallejo@itesm.mx , taylor@biology.ucla.edu

Conference: ICPR06

Abstract:

      In this work we explore the application of data mining techniques to the problem of acoustic recognition of bird species. Most bird song analysis tools produce a large amount of spectral and temporal attributes from the acoustic signal. The identification of distinctive features has become critical in resource constrained applications such as habitat monitoring by sensor networks. Reducing computational requirements makes affordable to run a classifier on devices with power consumption constraints, such as nodes in a sensor network. Experimental results demonstrate that considerable dimensionality reduction can be achieved without significant loss in classification efficiency.

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IEEE Computer Society Link


Citation:

Erika Vilches, Ivan A. Escobar, Edgar E. Vallejo, Charles E. Taylor, "Data Mining Applied to Acoustic Bird Species Recognition," icpr, pp. 400-403, 18th International Conference on Pattern Recognition (ICPR'06), 2006.


Bibtex Reference

@article{10.1109/ICPR.2006.426,
author = {Erika Vilches and Ivan A. Escobar and Edgar E. Vallejo and Charles E. Taylor},
title = {Data Mining Applied to Acoustic Bird Species Recognition},
journal = {icpr},
volume = {3},
year = {2006},
issn = {1051-4651},
pages = {400-403},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICPR.2006.426},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}