Abstract
This report presents a dual-level knowledge distillation framework with multi-teacher guidance for low-complexity acoustic scene classification (ASC) in DCASE2025 Task 1. We propose a distillation strategy that jointly transfers both soft logits and intermediate feature representations. Specifically, we pre-trained PaSST and CP-ResNet models as teacher models. Logits from teachers are averaged to generate soft targets, while one CP-ResNet is selected for feature-level distillation. This enables the compact student model (CP-Mobile) to capture both semantic distribution and structural information from teacher guidance. Experiments on the TAU Urban Acoustic Scenes 2022 Mobile dataset (development set) demonstrate that our submitted systems achieve up to 59.30\% accuracy.
Abstract (translated)
该报告提出了一种双层知识蒸馏框架，采用多教师指导，用于DCASE2025任务1中的低复杂度声学场景分类（ASC）。我们提出了一种同时传输软对数和中间特征表示的知识迁移策略。具体而言，我们将PaSST和CP-ResNet模型作为预训练的教师模型。来自各教师模型的对数通过平均生成软目标，而一个CP-ResNet用于特征级别的蒸馏。这使得紧凑的学生模型（CP-Mobile）能够从教师指导下捕获语义分布和结构信息。在TAU Urban Acoustic Scenes 2022 Mobile数据集（开发集）上的实验表明，我们提交的系统达到了最高59.30%的准确率。
URL
https://arxiv.org/abs/2507.19557
