Główne pojęcia
Creating a unified benchmark for classifying bird vocalizations in avian bioacoustics.
Streszczenie
The BirdSet benchmark addresses challenges in avian bioacoustics by consolidating research efforts to classify bird vocalizations. Deep learning models play a crucial role in diagnosing environmental health and biodiversity through analyzing bird calls. The benchmark aims to harmonize open-source bird recordings into a curated dataset collection, facilitating the evaluation of model performance across different tasks. By establishing baseline results of current models, BirdSet enhances comparability, guides data collection, and increases accessibility for newcomers to avian bioacoustics.
Statystyki
Deep learning models have significantly reduced the workload of experts in avian bioacoustics.
Recordings range from active focal recordings targeting specific bird species to passive soundscape recordings encompassing additional ambient sounds.
There is a lack of consistent dataset and task selection posing barriers to reproducibility, comparability, and accessibility.
DL models primarily operate on spectrograms requiring manual conversion from raw audio to images.
Cytaty
"Avian diversity is a crucial indicator of environmental health." - Sekercioglu et al., 2016
"Recent advancements in deep learning have significantly reduced the workload of experts in avian bioacoustics." - Stowell, 2021
"We provide an overview of the current state-of-the-art challenges and briefly describe our strategies to navigate these obstacles." - Rauch et al., 2023b