Publikation
Active Learning in Multi-label Classification of Bioacoustic Data
Hannes Kath; Thiago Gouvea; Daniel Sonntag
In: Dietmar Seipel; Alexander Steen (Hrsg.). KI 2024: Advances in Artificial Intelligence. German Conference on Artificial Intelligence (KI-2024), 47th German Conference on AI, Würzburg, Germany, September 25–27, 2023, Proceedings, located at 47th German Conference on AI, September 25-27, Würzburg, Germany, Germany, LNAI, Springer, Heidelberg, 9/2024.
Zusammenfassung
Passive Acoustic Monitoring (PAM) has become a key technology in wildlife monitoring, providing vast amounts of acoustic data.
The recording process naturally generates multi-label datasets; however, due to the significant annotation time required, most available datasets use exclusive labels. While active learning (AL) has shown the potential to speed up the annotation process of multi-label PAM data, it lacks standardized performance metrics across experimental setups. We present a novel performance metric for AL, the `speedup factor', which remains constant across experimental setups.
It quantifies the fraction of samples required by an AL strategy compared to random sampling to achieve equivalent model performance.
Using two multi-label PAM datasets, we investigate the effects of class sparsity, ceiling performance, number of classes, and different AL strategies on AL performance. Our results show that AL performance is superior on datasets with sparser classes, lower ceiling performance, fewer classes, and when using uncertainty sampling strategies.