Open AccessAdvances in Automatic Bird Species Recognition from Environmental Audio
Author(s) -
Xueyan Dong,
Jingpeng Jia
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1544/1/012110
Subject(s) - computer science , data science , terabyte , big data , task (project management) , software , scale (ratio) , feature extraction , bioacoustics , noise (video) , artificial intelligence , machine learning , data mining , image (mathematics) , geography , telecommunications , engineering , systems engineering , cartography , programming language , operating system
Bioacoustics has recently become one of the “big data” research topics since many bird monitoring projects have collected terabytes of audio using remote sensors. The challenge in recent years is to develop algorithms to realize fully automatic recognition of bird species through analysing environmental recordings. A number of approaches directly draw on experience of effective algorithms in signal processing and image processing areas. They seem working well for small data or lab data, however, the outcomes for large-scale environmental data shows a big gap between theoretical experiments and real applications. To provide possible clues for future research, we review the state-of-art development in automated bird species recognition, and identify wide range of algorithms on noise removal, bird call detection, feature extraction for classification. The significant software tools and publicly available datasets for the task are presented. This survey can be valuable for new researchers who are about to start the journey with birdsong analysis.