A machine learning system that classifies audio recordings of bird calls and songs, built with fine-tuned Wav2Vec2 and a feature pipeline tailored for noisy field data.
Designed to help birdwatchers, researchers, and conservationists quickly identify species from audio captured in real-world environments.
A production-ready pipeline with insights into model performance, dataset curation, and deployment readiness.
Achieved higher classification accuracy on field audio while maintaining low latency for inference.
Contextual analysis showed improved species separation across similar-sounding calls and reduced false positives on noisy backgrounds.