Abstract
Acoustic Scene Classification (ASC) faces challenges in generalizing across recording devices, particularly when labeled data is limited. The DCASE 2024 Challenge Task 1 highlights this issue by requiring models to learn from small labeled subsets recorded on a few devices. These models need to then generalize to recordings from previously unseen devices under strict complexity constraints. While techniques such as data augmentation and the use of pre-trained models are well-established for improving model generalization, optimizing the training strategy represents a complementary yet less-explored path that introduces no additional architectural complexity or inference overhead. Among various training strategies, curriculum learning offers a promising paradigm by structuring the learning process from easier to harder examples. In this work, we propose an entropy-guided curriculum learning strategy to address the domain shift problem in data-efficient ASC. Specifically, we quantify the uncertainty of device domain predictions for each training sample by computing the Shannon entropy of the device posterior probabilities estimated by an auxiliary domain classifier. Using entropy as a proxy for domain invariance, the curriculum begins with high-entropy samples and gradually incorporates low-entropy, domain-specific ones to facilitate the learning of generalizable representations. Experimental results on multiple DCASE 2024 ASC baselines demonstrate that our strategy effectively mitigates domain shift, particularly under limited labeled data conditions. Our strategy is architecture-agnostic and introduces no additional inference cost, making it easily integrable into existing ASC baselines and offering a practical solution to domain shift.
Abstract (translated)
声场景分类（ASC）在泛化到不同录音设备时面临挑战，尤其是在标记数据有限的情况下。DCASE 2024 挑战赛任务1突显了这一问题，要求模型从少数设备录制的小型标签子集中学习，并且需要这些模型在严格的复杂性约束下对来自之前未见过的设备的记录进行泛化。虽然诸如数据增强和使用预训练模型等技术已广泛应用于改进模型的泛化能力，但优化训练策略提供了一种补充而较少被探索的方法，这种方法不会引入额外的架构复杂性和推理开销。 在各种训练策略中，课程学习通过从简单的例子到更难的例子来结构化学习过程，提供了一个有前景的范例。在这项工作中，我们提出了一种基于熵引导的课程学习策略，以解决数据高效ASC中的领域偏移问题。具体来说，我们通过计算辅助领域分类器估计的设备后验概率的香农熵来量化每个训练样本在设备领域的预测不确定性。利用熵作为领域不变性的代理，该课程从高熵样本开始，并逐渐纳入低熵、特定领域的样本，以促进泛化表示的学习。 我们在多个DCASE 2024 ASC基线上进行了实验结果验证，证明我们的策略有效地缓解了域偏移问题，尤其是在标记数据有限的情况下。我们的策略是架构无关的，并且不会引入额外的推理成本，使其易于集成到现有的ASC基准中，并提供了一种实用的解决方案来解决领域偏移问题。
URL
https://arxiv.org/abs/2509.11168
