用颜色增强声谱图，提升鸟类鸣叫声分类准确率
Improving Bird Classification with Primary Color Additives
- 将频段信息以彩色添加到声谱图中，增强不同鸟种的视觉差异
- 在BirdCLEF 2024数据集上，F1提升7.3%，ROC-AUC提升6.2%
- 适合处理低信噪比或多种鸟重叠的复杂录音场景
我们研究利用鸟类鸣叫声进行物种分类的问题，该任务因环境噪声、声音重叠和标签缺失而极具挑战。现有模型在低信噪比或多物种录音上表现不佳。我们假设可通过可视化音高模式、节奏速度和重复性（统称动机）来识别鸟类。深度学习模型应用于声谱图虽有效，但跨物种相似动机仍导致混淆。为此，我们通过主色添加剂将频率信息嵌入声谱图，增强物种区分度。实验表明，该方法相比无色彩化模型显著提升性能，超越了BirdCLEF 2024冠军，F1提高7.3%，ROC-AUC提升6.2%，CMAP提升6.6%。结果验证了色彩化融合频段信息的有效性。
原文摘要 · Abstract (English)
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.
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