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<identifier>oai:HAL:hal-00840741v1</identifier>
<datestamp>2017-12-21</datestamp>
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<publisher>HAL CCSD</publisher>
<title lang=en>Song-based Classification techniques for Endangered Bird Conservation</title>
<creator>Stattner, Erick</creator>
<creator>Segretier, Wilfried</creator>
<creator>Collard, Martine</creator>
<creator>Hunel, Philippe</creator>
<creator>Vidot, Nicolas</creator>
<contributor>Laboratoire de Mathématiques Informatique et Applications (LAMIA) ; Université des Antilles et de la Guyane (UAG)</contributor>
<description>6 pages, 4 figures. In ICML 2013 Workshop on Machine Learning for Bioacoustics</description>
<description>International audience</description>
<source>ICML 2013 Workshop on Machine Learning for Bioacoustics</source>
<source>ICML 2013 Workshop on Machine Learning for Bioacoustics</source>
<coverage>Atlanta, United States</coverage>
<identifier>hal-00840741</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-00840741</identifier>
<source>https://hal.archives-ouvertes.fr/hal-00840741</source>
<source>ICML 2013 Workshop on Machine Learning for Bioacoustics, Jun 2013, Atlanta, United States. pp.1-6, 2013</source>
<identifier>ARXIV : 1306.5349</identifier>
<relation>info:eu-repo/semantics/altIdentifier/arxiv/1306.5349</relation>
<language>en</language>
<subject>[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG]</subject>
<type>info:eu-repo/semantics/conferenceObject</type>
<type>Conference papers</type>
<description lang=en>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.</description>
<date>2013-06-20</date>
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