Publikation
BirdNET-Annotator: AI-Assisted Strong Labelling of Bird Sound Datasets
Bengt Lüers; Patricia P. Serafini; Ivan Braga Campos; Thiago Gouvea; Daniel Sonntag
In: 3rd Annual AAAI Workshop on AI to Accelerate Science and Engineering. AAAI Workshop on AI to Accelerate Science and Engineering (AI2ASE), located at AAAI, February 26, Vancouver, BC, Canada, 2024.
Zusammenfassung
Monitoring biodiversity in biosphere reserves is challenging due to the vast regions to be monitored. Thus, conservation- ists have resorted to employing passive acoustic monitoring (PAM), which automates the audio recording process. PAM can create large, unlabeled datasets, but deriving knowledge from such recordings is usually still done manually.
Machine learning enables the detection of vocalizations of species automatically, allowing summarizing the biodiversity in an area in terms of species richness. While pre-trained neu- ral network models for bird vocalization detection exist, they are often not-reliable enough to do way with the need for manual labeling of audio files.
In this paper, we present BirdNET-Annotator, a tool for AI- assisted labeling of audio datasets co-developed by ecoacous- tics and ML experts. BirdNET-Annotator runs in the cloud free of charge, enabling end users to scale beyond the limita- tions of their local hardware. We evaluated the performance of our solution in the context of its intended workflow and found a reduction in annotation times. While our results show that our application now meets the user requirements, there are still opportunities to seize for additional performance and usability improvement.
Our application illustrates how large, pre-trained neural mod- els can be integrated into the workflow of domain experts when packaged in a user-friendly manner. We observe that although our solution adds a step to the preexisting workflow, the overall annotation speed is significantly improved. This hints at further improvement to be realized in the future by consolidating more steps of the workflow into fewer tools.