Improving Bird Classification with Primary Color Additives
This addresses the problem of accurate bird classification for ecologists and conservationists, offering an incremental enhancement to existing methods.
The paper tackles bird species classification from song recordings by embedding frequency information into spectrograms using primary color additives, resulting in improvements of 7.3% in F1, 6.2% in ROC-AUC, and 6.6% in CMAP over the BirdCLEF 2024 winner.
We address the problem of classifying bird species using their song recordings, a challenging task due to environmental noise, overlapping vocalizations, and missing labels. Existing models struggle with low-SNR or multi-species recordings. We hypothesize that birds can be classified by visualizing their pitch pattern, speed, and repetition, collectively called motifs. Deep learning models applied to spectrogram images help, but similar motifs across species cause confusion. To mitigate this, we embed frequency information into spectrograms using primary color additives. This enhances species distinction and improves classification accuracy. Our experiments show that the proposed approach achieves statistically significant gains over models without colorization and surpasses the BirdCLEF 2024 winner, improving F1 by 7.3%, ROC-AUC by 6.2%, and CMAP by 6.6%. These results demonstrate the effectiveness of incorporating frequency information via colorization.