Development of a species identification system of Japanese bats from echolocation calls using convolutional neural networks ...
(Uploaded by Plazi for the Bat Literature Project) Bats inhabit all continents except Antarctica, and they have enormous potential as bioindicators. Therefore, monitoring bats helps us to understand the surrounding environmental changes. However, bats are nocturnal, which makes it difficult to visua...
| Main Authors: | , , , , , |
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| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
Zenodo
2021
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| Subjects: | |
| Online Access: | https://dx.doi.org/10.5281/zenodo.13468254 https://zenodo.org/doi/10.5281/zenodo.13468254 |
| Summary: | (Uploaded by Plazi for the Bat Literature Project) Bats inhabit all continents except Antarctica, and they have enormous potential as bioindicators. Therefore, monitoring bats helps us to understand the surrounding environmental changes. However, bats are nocturnal, which makes it difficult to visually monitor their behavior. This paper proposes a bat species identifier method based on the analysis of ultrasound called echolocation calls, which is a promising method to monitor bats' activity levels effectively. We develop a robust method to identify the bat species with improved accuracy by analyzing their echolocation calls. First, 1400 sound files with four families, 13 genera, and 30 species were recorded in Japan and the Jincheon-gun in South Korea from 1999 to 2019. Bat echolocation calls were detected from the sound files and used to generate 54,525 spectrograms by applying short-time Fourier transform. We developed a deep learning–based bat species identifier using convolutional neural networks with ... |
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