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We present working notes on transfer learning with semi-supervised datasetannotation for the BirdCLEF 2023 competition, focused on identifying Africanbird species in recorded soundscapes. Our approach utilizes existingoff-the-shelf models, BirdNET and MixIT, to address representation and labelingchallenges in the competition. We explore the embedding space learned byBirdNET and propose a process to derive an annotated dataset for supervisedlearning. Our experiments involve various models and feature engineeringapproaches to maximize performance on the competition leaderboard. The resultsdemonstrate the effectiveness of our approach in classifying bird species andhighlight the potential of transfer learning and semi-supervised datasetannotation in similar tasks.

Transfer Learning with Semi-Supervised Dataset Annotation for Birdcall Classification