Transfer Learning with Semi-Supervised Dataset Annotation for Birdcall
  Classification
Anthony Miyaguchi Nathan Zhong Murilo Gustineli Chris Hayduk
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.