Identifying Birds by Sound: Large-scale Acoustic Event Recognition for Avian Activity Monitoring

Automated observation of avian vocal activity and species diversity can be a transformative tool for ornithologists, conservation biologists, and bird watchers to assist in long-term monitoring of critical environmental niches. Deep artificial neural networks have surpassed traditional classifiers in the field of visual recognition and acoustic event classification. Still, deep neural networks require expert knowledge to design, train, and test powerful models. With this constraint and the requirements of future applications in mind, an extensive research platform for automated avian activity monitoring was developed: BirdNET. The resulting benchmark system yields state-of-the-art scores across various acoustic domains and was used to develop expert tools and public demonstrators that can help to advance the democratization of scientific progress and future conservation efforts.

Product details

• Authors: Stefan Kahl
• Publisher: Universitätsverlag Chemnitz
• Languages: English
• Product format: Paperback / Softback
• Released: 01.01.2020
• EAN: 9783961001101
• ISBN: 978-3-96100-110-1
• No. of pages: 304
• Dimensions: 148 mm x 18 mm x 210 mm
• Weight: 472 g
• Series: Wissenschaftliche Schriftenreihe Dissertationen der Medieninformatik
• Subject: Natural sciences, medicine, IT, technology > IT, data processing