AI 中文总结
针对小样本开放集音频分类的原型易受未知类别污染问题，提出两阶段直推式方法，结合内类度加权与解耦评分，在三个音频数据集上取得最优结果。
AI 中文摘要
小样本开放集音频分类要求用少量标记的支持样本对已知类别的查询样本进行分类，同时拒绝未知类别的查询样本。直推式推理通过联合观察全部未标记的查询集来改进原型估计，但标准直推式更新无法区分查询样本的已知类别与未知类别，导致原型易受开放集污染。针对未知类别样本，我们结合潜在内类度加权与解耦评分，提出一种在冻结音频编码器上运行的两阶段直推式方法：首先为每个查询样本分配潜在内类度评分，对大概率属于未知类别的样本进行降权，使原型优化主要由已知类别的证据驱动；随后，结合支持集交叉熵、内类度加权条件熵最小化与内类度加权边际熵最大化的直推式损失直接优化优化后的原型，同时开放集拒绝采用先验自适应自由能评分，该评分会根据未知类别的先验比例调整阈值，将检测与分类解耦。在三个音频数据集上的实验表明，我们的方法在多种实验条件下均取得了小样本开放集音频分类的最优结果。
英文摘要
Few-shot Open-set audio classification requires classifying query samples from known classes with a few labeled support samples while rejecting query samples from unknown classes. Transductive inference jointly observes the full unlabeled query set to improve prototype estimation, yet standard transductive updates do not distinguish known from unknown query samples, leaving prototypes vulnerable to open-set contamination. Drawing on latent-inlierness weighting and decoupled scoring for unknown-class samples, we propose a two-phase transductive method operating over a frozen audio encoder. First, each query sample is assigned a latent inlierness score that down-weights likely unknown-class samples, so that prototype refinement is driven primarily by known-class evidence. The refined prototypes are then directly optimized on a transductive loss combining support cross-entropy, inlierness-weighted conditional entropy minimization, and inlierness-weighted marginal entropy maximization, while open-set rejection uses a prior-adaptive free-energy score that adjusts its threshold with the prior proportion of unknown-class samples, decoupling detection from classification. Experiments on three audio datasets show our method achieves state-of-the-art results for few-shot open-set audio classification under multiple experimental conditions.
CommentsAccepted for publication in IEEE ICSPCC 2026. 6 pages, 1 figure
