LifeCLEF Bird Identification Task 2016: The arrival of Deep learning

Hervé Goëau 1 Hervé Glotin 2 Willem-Pier Vellinga 3 Robert Planqué 4 Alexis Joly 1
1 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : The LifeCLEF bird identification challenge provides a large-scale testbed for the system-oriented evaluation of bird species identification based on audio recordings. One of its main strength is that the data used for the evaluation is collected through Xeno-Canto, the largest network of bird sound recordists in the world. This makes the task closer to the conditions of a real-world application than previous, similar initiatives. The main novelty of the 2016-th edition of the challenge was the inclusion of soundscape recordings in addition to the usual xeno-canto recordings that focus on a single foreground species. This paper reports the methodology of the conducted evaluation, the overview of the systems experimented by the 6 participating research groups and a synthetic analysis of the obtained results.
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Submitted on : Wednesday, October 5, 2016 - 12:40:48 PM
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  • HAL Id : hal-01373779, version 1



Hervé Goëau, Hervé Glotin, Willem-Pier Vellinga, Robert Planqué, Alexis Joly. LifeCLEF Bird Identification Task 2016: The arrival of Deep learning. CLEF: Conference and Labs of the Evaluation Forum, Sep 2016, Évora, Portugal. pp.440-449. ⟨hal-01373779⟩



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