Abstract
Acoustic scene classification (ASC) suffers from device-induced domain shift, especially when labels are limited. Prior work focuses on curriculum-based training schedules that structure data presentation by ordering or reweighting training examples from easy-to-hard to facilitate learning; however, existing curricula are static, fixing the ordering or the weights before training and ignoring that example difficulty and marginal utility evolve with the learned representation. To overcome this limitation, we propose the Dynamic Dual-Signal Curriculum (DDSC), a training schedule that adapts the curriculum online by combining two signals computed each epoch: a domain-invariance signal and a learning-progress signal. A time-varying scheduler fuses these signals into per-example weights that prioritize domain-invariant examples in early epochs and progressively emphasize device-specific cases. DDSC is lightweight, architecture-agnostic, and introduces no additional inference overhead. Under the official DCASE 2024 Task~1 protocol, DDSC consistently improves cross-device performance across diverse ASC baselines and label budgets, with the largest gains on unseen-device splits.
Abstract (translated)
声学场景分类（ASC）面临着由于设备引起的领域偏移问题，尤其是在标签有限的情况下。之前的工作主要集中在基于课程的学习计划上，通过按难度从易到难排序或重新加权训练示例来结构化数据呈现以促进学习；然而，现有的课程是静态的，在训练前固定了排序或权重，并忽略了随着所学表示的变化，样本难度和边际效用也会变化。为了克服这一限制，我们提出了一种动态双信号课程（DDSC）的训练计划，该计划通过结合每轮计算出的两个信号在线调整课程：领域不变性信号和学习进度信号。一个时变调度器将这些信号融合成每个示例的权重，在早期时期优先考虑领域不变性的示例，并逐步强调设备特定的情况。 DDSC轻量级、与架构无关且不增加额外的推理开销。在官方DCASE 2024 Task 1协议下，DDSC在各种ASC基线和标签预算上持续提高了跨设备性能，在未见过设备的数据集上取得了最大的改进。
URL
https://arxiv.org/abs/2510.17345
