AI 中文总结
针对生物声学主动学习中目标叫声稀疏、分布呈长尾状及时间粒度不匹配问题，提出BADGE - Greedy - DPP确定性批量选择器，利用梯度嵌入贪心最大化体积，在鬣狗叫声数据集上性能优于其他策略。
AI 中文摘要
生物声学叫声类型分类依赖昂贵的专家注释。主动学习可通过选择少量片段进行注释并利用标注片段训练分类器来减轻负担。该设置具有挑战性，目标叫声极为稀疏且叫声类型分布呈长尾状，因此必须将严格预算花在少数罕见且信息丰富的片段上。我们提出BADGE - Greedy - DPP，一种确定性批量选择器，它贪婪地添加使批量跨度体积最大的片段。由于对数体积目标是次模的，贪心规则保证批量值至少为该目标最优值的(1 - 1/e) 。任务中还存在时间粒度不匹配问题，BADGE构建在按帧应用时自然解决了该问题。在稀疏、不平衡的鬣狗叫声类型数据集上进行10次运行，BADGE - Greedy - DPP在所有比较的查询策略中实现了最佳的整体和罕见叫声类型性能。
英文摘要
Bioacoustic call-type classification relies on costly expert annotation. Active learning can reduce this burden by selecting a small batch of segments for expert annotation and using the labeled segments for training the classifier. The setting is hard: the target calls are extremely sparse and the call-type distribution is long-tailed, so a tight budget must be spent on the few rare, informative segments. We propose BADGE-Greedy-DPP, a deterministic batch selector that greedily adds the segment whose BADGE gradient embedding most enlarges the volume spanned by the batch; because this log-volume objective is submodular, the greedy rule guarantees a batch value at least a (1-1/e) fraction of the optimum of this objective, a guarantee not provided by BADGE's existing k-means++ and MCMC DPP sampling heuristics. There is also a temporal granularity mismatch in the task. The acquisition function scores whole segments, yet the informative frames inside them are few. Uniform averaging therefore washes them out. The BADGE construction naturally addresses this mismatch when applied frame-wise, as prediction residuals weight the aggregated pseudo-gradient, so confidently predicted no-call frames contribute little while a single uncertain rare-call frame can still set the segment's direction. Across 10 runs on a sparse, imbalanced hyena call-type dataset, BADGE-Greedy-DPP achieves the best overall and rare-call-type performance among all compared query strategies, including MFFT, the strongest non-BADGE baseline, and the two vanilla BADGE traversals.
CommentsAccepted to DCASE workshop 2026
