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.

翻译：声学场景分类（ASC）面临设备引起的域偏移问题，在标注数据有限时尤为突出。现有研究主要基于课程学习的训练策略，通过按从易到难的顺序排列或重加权训练样本来组织数据呈现以促进学习；然而，现有课程是静态的，在训练前固定样本顺序或权重，忽略了样本难度与边际效用会随学习到的表征动态演变。为克服这一局限，我们提出动态双信号课程学习（DDSC），这是一种通过结合每轮训练周期计算的两个信号——域不变性信号与学习进度信号——在线自适应调整课程的训练策略。时变调度器将这两个信号融合为样本级权重，在训练早期优先关注域不变样本，并逐步加强对设备特定案例的重视。DDSC具有轻量级、架构无关的特性，且不引入额外推理开销。在DCASE 2024任务1的官方评测协议下，DDSC在不同ASC基线模型和标注预算条件下均能持续提升跨设备性能，在未见设备数据划分上取得最显著的改进。