Improving Acoustic Scene Classification in Low-Resource Conditions

2024-12-30 · Zhi Chen, Yun-Fei Shao, Yong Ma, Mingsheng Wei, Le Zhang, Wei-Qiang Zhang

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.

📄 PDF Abstract BibTeX arXiv:2412.20722

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Tasks

Acoustic Scene Classification Classification Data Augmentation Knowledge Distillation Model Compression Quantization Scene Classification

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