@article {Qian:2017:1610-1928:361,
title = "Active Learning for Bird Sounds Classification",
journal = "Acta Acustica united with Acustica",
parent_itemid = "infobike://dav/aaua",
publishercode ="dav",
year = "2017",
volume = "103",
number = "3",
publication date ="2017-05-01T00:00:00",
pages = "361-364",
itemtype = "ARTICLE",
issn = "1610-1928",
url = "https://www.ingentaconnect.com/content/dav/aaua/2017/00000103/00000003/art00002",
doi = "doi:10.3813/AAA.919064",
author = "Qian, Kun and Zhang, Zixing and Baird, Alice and Schuller, Bj{\"o}rn",
abstract = "It has been shown that automatic bird sound recognition can be an extremely useful tool for ornithologist and ecologists, allowing for a deeper understanding of; mating, evolution, local biodiversity and even climate change. For a robust and efficient recognition model, a large amount
of labelled data is needed, requiring a time consuming and costly effort by expert-human annotators. To reduce this, we introduce for the first time, active learning, for automatic selection of the most informative data for training the recognition model. Experimental results show that our
proposed; sparse-instance-based and least-confidence-score-based active learning methods reduce respectively 16.0% and 35.2% human annotated samples than compared to passive learning methods, achieving an acceptable performance (unweighted average recall > 85%), when recognising
the sound of 60 different species of birds. \textcopyright 2017 The Author(s). Published by S. Hirzel Verlag \textperiodcentered EAA. This is an open access article under the terms of the Creative Commons Attribution (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/).",
}