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Open Access Active Learning for Bird Sounds Classification

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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.

© 2017 The Author(s). Published by S. Hirzel Verlag · 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/).

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Document Type: Research Article

Publication date: May 1, 2017

This article was made available online on April 5, 2017 as a Fast Track article with title: "Active Learning for Bird Sounds Classification".

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  • Acta Acustica united with Acustica, published together with the European Acoustics Association (EAA), is an international, peer-reviewed journal on acoustics. It publishes original articles on all subjects in the field of acoustics, such as general linear acoustics, nonlinear acoustics, macrosonics, flow acoustics, atmospheric sound, underwater sound, ultrasonics, physical acoustics, structural acoustics, noise control, active control, environmental noise, building acoustics, room acoustics, acoustic materials, acoustic signal processing, computational and numerical acoustics, hearing, audiology and psychoacoustics, speech, musical acoustics, electroacoustics, auditory quality of systems. It reports on original scientific research in acoustics and on engineering applications. The journal considers scientific papers, technical and applied papers, book reviews, short communications, doctoral thesis abstracts, etc. In irregular intervals also special issues and review articles are published.
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