Automated Bioacoustic Monitoring for South African Bird Species on Unlabeled Data

Autor:	Doell, Michael, Kuehn, Dominik, Suessle, Vanessa, Burnett, Matthew J., Downs, Colleen T., Weinmann, Andreas, Hergenroether, Elke
Rok vydání:	2024
Předmět:	Computer Science - Sound Computer Science - Computer Vision and Pattern Recognition Electrical Engineering and Systems Science - Audio and Speech Processing
Zdroj:	International Conferences in Central Europe on Computer Graphics, Visualization and Computer Vision 2024
Druh dokumentu:	Working Paper
Popis:	Analyses for biodiversity monitoring based on passive acoustic monitoring (PAM) recordings is time-consuming and challenged by the presence of background noise in recordings. Existing models for sound event detection (SED) worked only on certain avian species and the development of further models required labeled data. The developed framework automatically extracted labeled data from available platforms for selected avian species. The labeled data were embedded into recordings, including environmental sounds and noise, and were used to train convolutional recurrent neural network (CRNN) models. The models were evaluated on unprocessed real world data recorded in urban KwaZulu-Natal habitats. The Adapted SED-CRNN model reached a F1 score of 0.73, demonstrating its efficiency under noisy, real-world conditions. The proposed approach to automatically extract labeled data for chosen avian species enables an easy adaption of PAM to other species and habitats for future conservation projects. Comment: preprint
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.13579 Zobrazit plný text záznamu View this record from Arxiv