Abstract
Acoustic scene classification (ASC) models on edge devices typically operate under fixed class assumptions, lacking the transferability needed for real-world applications that require adaptation to new or refined acoustic categories. We propose ContrastASC, which learns generalizable acoustic scene representations by structuring the embedding space to preserve semantic relationships between scenes, enabling adaptation to unseen categories without retraining. Our approach combines supervised contrastive fine-tuning of pre-trained models with contrastive representation distillation to transfer this structured knowledge to compact student models. Our evaluation shows that ContrastASC demonstrates improved few-shot adaptation to unseen categories while maintaining strong closed-set performance.
Abstract (translated)
声场景分类（ASC）模型在边缘设备上通常基于固定的类别假设运行，缺乏适应新或细化的声学类别的可转移性，而这对于需要进行此类调整的实际应用来说是必需的。我们提出了一种名为ContrastASC的方法，该方法通过构建嵌入空间来保持不同场景之间的语义关系，从而学习通用化的声场景表示，使模型能够无需重新训练就适应未见过的类别。我们的方法结合了预训练模型的监督对比微调与对比表征蒸馏技术，将这种结构化知识转移到紧凑的学生模型中。评估结果表明，ContrastASC在保持强闭集性能的同时，展示了对未见类别的良好少样本自适应能力。
URL
https://arxiv.org/abs/2510.03728
