Advances in Automatic Bird Species Recognition from Environmental Audio

Abstract

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.

Mallard Detection Using Microphone Arrays Combined with Delay-and-Sum Beamforming for Smart and Remote Rice–Duck Farming

“…The position detection interval was 5 m horizontally and vertically. For Experiment C, the position that could not be calculated on the diagonal was merely (15,10). This field consists of a rectangular shape, unlike the square field for Experiment A.…”

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Design of Bird Sound Recognition Model Based on Lightweight

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