用确定性点过程选样本，让生物声学分类更省力又准确。
Determinantal point process sampling for bioacoustic active learning
- 结合不确定性与新颖性，用DPP选不重复的高质量样本批次。
- 在多个数据集上平均宏mAP达0.50，优于基线0.46。
- 适合需要高效标注的生态音频监测任务，尤其关注多样性覆盖。
生态声学监测产生海量音频数据，主动学习可有效降低标注负担并训练可靠的生物多样性分类器。本文提出CARE-DPP，一种提交至BioDCASE 2026生物声学主动学习挑战赛的批量主动学习方法。该方法融合类别平衡的预测不确定性与嵌入空间的新颖性，利用确定性点过程（DPP）目标选择高质量且无冗余的样本批次。不确定性与新颖性之间的权衡随标注预算递进调整：早期强调几何覆盖，后期逐渐侧重分类器不确定性。为缓解早期评分不可靠问题，DPP候选池混合优质样本与递减比例的随机探索样本。采用自适应获取策略，前期小批量，后期大批量。在BirdSet HSN、POW和UHH子集及ATBFL数据集上重复五次评估，CARE-DPP获得平均发展AULC为0.50（宏mAP），高于官方CoreSet基线的0.46。消融实验表明，DPP批处理多样化和自适应获取策略贡献最大。
原文摘要 · Abstract (English)
Eco-acoustic monitoring generates vast volumes of audio data, making active learning a promising approach for reducing annotation effort while efficiently training reliable biodiversity classifiers. This report presents CARE-DPP, a batch active-learning acquisition method submitted to BioDCASE Active Learning for Bioacoustics 2026 challenge. The method combines class-balanced predictive uncertainty with embedding-space novelty, while a determinantal point process (DPP) objective selects a high-quality and non-redundant acquisition batch. The uncertainty-novelty balance is annealed over the annotation budget: early cycles emphasize geometric coverage, whereas later cycles increasingly exploit classifier uncertainty. To mitigate unreliable early scores, the DPP candidate pool mixes top-quality candidates with a decreasing proportion of random exploration. An adaptive acquisition schedule uses smaller batches early and larger batches later. Evaluated over five repeats on the BirdSet HSN, POW and UHH subsets and on ATBFL, CARE-DPP obtains a mean development AULC of 0.50 for macro mAP, compared with 0.46 for the official CoreSet baseline. Ablations identify DPP batch diversification and the adaptive acquisition schedule as the largest contributors.
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