Song-based Classification techniques for Endangered Bird Conservation

Abstract : The work presented in this paper is part of a global framework which long term goal is to design a wireless sensor network able to support the observation of a population of endangered birds. We present the first stage for which we have conducted a knowledge discovery approach on a sample of acoustical data. We use MFCC features extracted from bird songs and we exploit two knowledge discovery techniques. One that relies on clustering-based approaches, that highlights the homogeneity in the songs of the species. The other, based on predictive modeling, that demonstrates the good performances of various machine learning techniques for the identification process. The knowledge elicited provides promising results to consider a widespread study and to elicit guidelines for designing a first version of the automatic approach for data collection based on acoustic sensors.
Type de document :
Communication dans un congrès
ICML 2013 Workshop on Machine Learning for Bioacoustics, Jun 2013, Atlanta, United States. pp.1-6, 2013
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https://hal.archives-ouvertes.fr/hal-00840741
Contributeur : Wilfried Segretier <>
Soumis le : mercredi 3 juillet 2013 - 06:32:02
Dernière modification le : mercredi 27 juillet 2016 - 14:48:48

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  • HAL Id : hal-00840741, version 1
  • ARXIV : 1306.5349

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Erick Stattner, Wilfried Segretier, Martine Collard, Philippe Hunel, Nicolas Vidot. Song-based Classification techniques for Endangered Bird Conservation. ICML 2013 Workshop on Machine Learning for Bioacoustics, Jun 2013, Atlanta, United States. pp.1-6, 2013. 〈hal-00840741〉

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