Automated species identification of frog choruses in environmental recordings using acoustic indices
Journal Publication ResearchOnline@JCUAbstract
Acoustic monitoring provides opportunities for scaling up bioacoustic study of vocal animals to greater temporal and spatial scales. However, the large amounts of audio that can be easily and efficiently collected necessitates automated methods of analysis to extract useful ecological data. Acoustic indices have been used in spectrographic visualisation of long environmental recordings to successfully identify many biological sounds from their acoustic patterns and features. In particular, the choruses of several frog species are conspicuous in these spectrogram images which suggests that acoustic indices may be useful for detecting species in automated sound classification algorithms. The aim of this study was to investigate the use of acoustic indices as predictors in classification models for automated identification of frog species in environmental sound recordings from breeding habitats in north Queensland, Australia. Three types of classification models (random forests, support vector machines and gradient boosting) were trained and validated on a data set of 3274 1-minute audio segments labelled for the presence or absence of calling of 12 target frog species, and a feature set of 11 acoustic indices calculated on frequency bins of bandwidth 43.1 Hz. Classification performance was high for all 12 target species on the validation data set held out from the labelled training data (precision range 0.90-1.00 and recall range 0.83-0.99). However, performance declined for most target species when predicting frog calling on a further test data set taken from unseen recordings from the same sites. Best prediction results on the test data were achieved for species with the most training data, indicating accuracy may be improved by increasing training data, and this method is best suited to predicting chorusing of common species.
Journal
Ecological Indicators
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Volume
119
ISBN/ISSN
1872-7034
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Pages Count
7
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Publisher
Elsevier
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EISSN
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DOI
10.1016/j.ecolind.2020.106852