Prof. Hsueh-Wen Chang and Ph.D. Candidate Shih-Hung Wu from National Sun Yat-Sen University, Taiwan, Dr. Ruey-Shing Lin, Assistant Researcher Jerome Chie-Jen Ko from the Endemic Species Research Institute, and Ms. Wen-Ling Tsai from Yushan National Park Headquarters have published a paper in Biodiversity Data Journal, detailing their use of AI to detect 6 million bird songs.

Compared to traditional observation-based methods, passive acoustic monitoring using automatic recorders to capture wildlife sounds provides cost-effective, long-term, and systematic alternative for long-term biodiversity monitoring. The authors deployed six recorders in Yushan National Park, Taiwan, a subtropical montane forest habitat with elevations ranging from 1,200 to 2,800 meters.

From 2020 to 2021, they recorded nearly 30,000 hours of audio files with abundant biological information. However, analyzing this vast dataset is challenging and requires more than human effort alone.

To tackle this challenge, the authors utilized deep learning technology to develop an AI tool called SILIC that can identify species by sound. SILIC can quickly pinpoint the precise timing of each animal call within the audio files. After several optimizations, the tool is now capable of recognizing 169 species of wildlife native to Taiwan, including 137 bird species, as well as frogs, mammals, and reptiles.