Abstract
Acoustic Scene Classification (ASC) identifies an environment based on an audio signal. This paper explores ASC in low-resource conditions and proposes a novel model, DS-FlexiNet, which combines depthwise separable convolutions from MobileNetV2 with ResNet-inspired residual connections for a balance of efficiency and accuracy. To address hardware limitations and device heterogeneity, DS-FlexiNet employs Quantization Aware Training (QAT) for model compression and data augmentation methods like Auto Device Impulse Response (ADIR) and Freq-MixStyle (FMS) to improve cross-device generalization. Knowledge Distillation (KD) from twelve teacher models further enhances performance on unseen devices. The architecture includes a custom Residual Normalization layer to handle domain differences across devices, and depthwise separable convolutions reduce computational overhead without sacrificing feature representation. Experimental results show that DS-FlexiNet excels in both adaptability and performance under resource-constrained conditions.
Abstract (translated)
声学场景分类（ASC）是一种基于音频信号识别环境的技术。本文探讨了在低资源条件下进行声学场景分类，并提出了一种新型模型DS-FlexiNet，该模型结合了MobileNetV2中的深度可分离卷积和ResNet启发式的残差连接，在效率与准确性之间取得了平衡。为了应对硬件限制和设备异质性问题，DS-FlexiNet采用量化感知训练（QAT）进行模型压缩，并使用诸如自动设备脉冲响应（ADIR）和频谱混合样式增强（FMS）等数据增强方法来提高跨设备泛化能力。从十二个教师模型中通过知识蒸馏进一步提升了在未见过的设备上的性能表现。该架构还包括一个自定义的残差归一化层，用于处理不同设备之间的领域差异，并且深度可分离卷积减少了计算负担而不牺牲特征表示的能力。 实验结果表明，在资源受限条件下，DS-FlexiNet无论是在适应性还是性能方面均表现出色。
URL
https://arxiv.org/abs/2412.20722
