Localized time-frequency representation learning for bioacoustic classification in complex soundscapes
The research proposes a semi-supervised acoustic bird detector that handles time-overlapping calls and requires few labeled samples. It uses energy-based segmentation, auto-encoding for compression, and contrastive learning to create robust embeddings. This method outperforms BirdNET on 103 species by leveraging self-supervision from large soundscape data.