DocumentCode :
2603252
Title :
Data Mining Applied to Acoustic Bird Species Recognition
Author :
Vilches, Erika ; Escobar, Ivan A. ; Vallejo, Edgar E. ; Taylor, Charles E.
Author_Institution :
Tecnologico de Monterrey, Atizapan de Zaragoza
Volume :
3
fYear :
0
fDate :
0-0 0
Firstpage :
400
Lastpage :
403
Abstract :
In this paper 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 it 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
Keywords :
acoustic signal processing; data mining; zoology; acoustic bird species recognition; data mining; dimensionality reduction; Acoustic applications; Acoustic devices; Acoustic sensors; Birds; Computer networks; Data mining; Energy consumption; Monitoring; Sensor phenomena and characterization; Signal analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 2006. ICPR 2006. 18th International Conference on
Conference_Location :
Hong Kong
ISSN :
1051-4651
Print_ISBN :
0-7695-2521-0
Type :
conf
DOI :
10.1109/ICPR.2006.426
Filename :
1699549
Link To Document :
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