用教师集成指导学生模型，实现高效低噪的移动端声音场景分类。
Ensemble-Guided Distillation for Compact and Robust Acoustic Scene Classification on Edge Devices
- 用教师集成+知识蒸馏，让小模型学大模型的判断逻辑。
- 在TAU数据集上达到移动端部署最优性能，准确率超现有方法。
- 适合资源受限的边缘设备，对噪声和设备差异鲁棒性强。
我们提出一种紧凑、可量化的声音场景分类（ASC）框架，结合高效的教师集成与知识蒸馏技术。学生网络采用堆叠的深度可分离「扩展-深度-投影」模块，并引入全局响应归一化以稳定训练，提升对设备和噪声变化的鲁棒性；全局池化头生成类别得分，便于边缘端高效推理。为注入更丰富的先验信息，构建多样化的教师模型集合，并学习两个互补的融合头：z1 使用学生式骨干预测各教师模型权重，z2 为轻量级MLP完成逐类得分融合。学生模型通过温度缩放的软标签与硬标签联合蒸馏，实现对教师集成决策几何的逼近。在TAU Urban Acoustic Scenes 2022 Mobile基准上评估，本方法在匹配边缘部署约束条件下达到当前最优表现，验证了其在移动端声音场景分类中的高性能与实用性。
原文摘要 · Abstract (English)
We present a compact, quantization-ready acoustic scene classification (ASC) framework that couples an efficient student network with a learned teacher ensemble and knowledge distillation. The student backbone uses stacked depthwise-separable "expand-depthwise-project" blocks with global response normalization to stabilize training and improve robustness to device and noise variability, while a global pooling head yields class logits for efficient edge inference. To inject richer inductive bias, we assemble a diverse set of teacher models and learn two complementary fusion heads: z1, which predicts per-teacher mixture weights using a student-style backbone, and z2, a lightweight MLP that performs per-class logit fusion. The student is distilled from the ensemble via temperature-scaled soft targets combined with hard labels, enabling it to approximate the ensemble's decision geometry with a single compact model. Evaluated on the TAU Urban Acoustic Scenes 2022 Mobile benchmark, our approach achieves state-of-the-art (SOTA) results on the TAU dataset under matched edge-deployment constraints, demonstrating strong performance and practicality for mobile ASC.
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