  We present working notes for the DS@GT team on transfer learning with pseudo
multi-label birdcall classification for the BirdCLEF 2024 competition, focused
on identifying Indian bird species in recorded soundscapes. Our approach
utilizes production-grade models such as the Google Bird Vocalization
Classifier, BirdNET, and EnCodec to address representation and labeling
challenges in the competition. We explore the distributional shift between this
year's edition of unlabeled soundscapes representative of the hidden test set
and propose a pseudo multi-label classification strategy to leverage the
unlabeled data. Our highest post-competition public leaderboard score is 0.63
using BirdNET embeddings with Bird Vocalization pseudo-labels. Our code is
available at https://github.com/dsgt-kaggle-clef/birdclef-2024
