How can computers learn to recognize birds from their sounds? As a Lead for BirdNET Technology within the K. Lisa Yang Center for Conservation Bioacoustics, I am trying to find an answer to this question. My research is mainly focused on the detection and classification of avian sounds using machine learning. Automated observation of avian vocal activity and species diversity can be a transformative tool for ornithologists, conservation biologists, and birdwatchers to assist in long-term monitoring of critical environmental niches.
With a background in computer vision and deep learning, I am mainly focusing on developing new methods to process large data collections of environmental sounds. After completing my master’s degree in Applied Computer Science in 2014, I became a research assistant at the Chemnitz University of Technology, Germany. I was involved in research projects covering human-computer and human-robot interaction, multimodal media retrieval, and mobile application development.
I joined the Yang Center in 2019, continuing my work on a bird sound recognition system I call BirdNET. My goal is to assist experts and citizen scientists in their work of monitoring and protecting our birds by developing a wide range of applications such as smartphone apps, public demonstrators, web interfaces, and robust analysis frameworks.
Degree(s): Ph.D. Deep learning for bioacoustics, Chemnitz University of Technology, Germany, in progress M.Sc. Applied Computer Science, Chemnitz University of Technology, Germany, 2013 B.Sc. Applied Computer Science, Chemnitz University of Technology, Germany, 2011
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