Uncertainty Calibration of Multi-Label Bird Sound Classifiers

The study on the calibration of multi-label bird sound classifiers highlights the importance of reliable bioacoustic monitoring for biodiversity assessment. By systematically benchmarking four advanced classifiers on the BirdSet benchmark, researchers found that calibration performance varied significantly across datasets and classes. Notably, Perch v2 and ConvNeXt$_{BS}$ demonstrated better global calibration, while AudioProtoPNet and BirdMAE were often overconfident. Interestingly, calibration appeared to improve for less frequent bird classes, suggesting that targeted calibration methods could enhance classifier performance. The findings underscore the necessity of well-calibrated uncertainty estimates in bioacoustics, as they directly influence decision-making in conservation strategies.