Abstract
As the most precious primate with only about 30 left, it is highly desirable to monitor the population of Hainan gibbon for the sake of effective protection. The traditional monitoring technique is facing the challenges of statistical hysteresis and a large number of labor costs, which makes it unsustainable. Deep learning based passive acoustic monitoring has shown its great potential in automatic detection of Hainan gibbon calls in Dufourq et al. (2021). Motivated by this progress, a bioacoustics presence detection network (BPDnet) was proposed for Hainan gibbon calls by tailoring acoustic signal pre-processing, feature extraction, label smoothing loss function, as well as data augmentation in the ResNet-based passive acoustic detection framework. In addition, a post-processing procedure was proposed to further improve the passive acoustic presence detection. Numerical results are presented to show that, the proposed BPDnet outperforms the baseline scheme of Dufourq et al. (2021) in terms of bio-acoustics presence detection accuracy, callback rate, F1-score by at least 12.9% when using the same 72-h test recording and test method without manual intervention. When manual post-processing is allowed, the proposed BPDnet provides us an almost perfect Hainan gibbon call presence detection scheme.
Published Version
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