Semi-supervised classification of bird vocalizations

A semi-supervised acoustic bird detector was developed for continuous monitoring, using few labeled samples. It outperforms the state-of-the-art BirdNET classifier with less training data, excelling in detecting time-overlapping calls. Challenges include suppressing false positives, but it demonstrates high precision with minimal labeled data, highlighting the potential of self-supervised learning in bioacoustic species classification. The detector was tested on Singapore's diverse soundscape, emphasizing the need for extensive labeled data, transfer learning, handling temporally-dense vocalizations, and addressing urban soundscapes. The text also discusses various methods and technologies for bird monitoring and species identification, including traditional census techniques, acoustic monitoring, and deep learning algorithms.