Recognizing bird species in audio recordings using deep convolutional neural networks
Karol Jerzy Piczak
AbstractThis paper summarizes a method for purely audio-based bird species recognition through the application of convolutional neural networks. The approach is evaluated in the context of the LifeCLEF 2016 bird identi�cation task - an open challenge conducted on a dataset containing 34 128 audio recordings representing 999 bird species from South America. Three di�erent network architectures and a simple ensemble model are considered for this task, with the ensemble submission achieving a mean average precision of 41.2% (o�cial score) and 52.9% for foreground species.
|Publication size in sheets||0.5|
|Book||Balog Krisztian, Cappellato Linda, Ferro Nicola, Macdonald Craig (eds.): Working Notes of CLEF 2016 - Conference and Labs of the Evaluation forum, CEUR Workshop Proceedings, vol. 1609, 2016, CEUR-WS, 1259 p.|
|Keywords in English||bird species identi�cation, convolutional neural networks, audio classi�cation, BirdCLEF 2016|
|Score||= 0.0, 13-12-2019, MonographChapterAuthor|
|Citation count*||20 (2020-09-14)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.