Adding metadata supervision (location, time, etc.) to bioacoustic models improves species detection and helps models handle domain shifts—showing that non-audio information from citizen science platforms can significantly boost real-world performance.
MetaPerch is a bioacoustic foundation model that uses recording metadata like location and time as additional training signals alongside audio. By leveraging species-habitat correlations in the metadata, the model learns more robust representations that better generalize to real-world acoustic monitoring scenarios with different environments and recording conditions.