Transfer Learning with Semi-Supervised Dataset Annotation for Birdcall Classification
This work addresses birdcall classification for ecological monitoring, but it is incremental as it applies known methods to a specific competition dataset.
The paper tackled bird species classification in soundscapes by using transfer learning with semi-supervised annotation from existing models, achieving effective results as demonstrated in the BirdCLEF 2023 competition.
We present working notes on transfer learning with semi-supervised dataset annotation for the BirdCLEF 2023 competition, focused on identifying African bird species in recorded soundscapes. Our approach utilizes existing off-the-shelf models, BirdNET and MixIT, to address representation and labeling challenges in the competition. We explore the embedding space learned by BirdNET and propose a process to derive an annotated dataset for supervised learning. Our experiments involve various models and feature engineering approaches to maximize performance on the competition leaderboard. The results demonstrate the effectiveness of our approach in classifying bird species and highlight the potential of transfer learning and semi-supervised dataset annotation in similar tasks.