Improving Acoustic Scene Classification in Low-Resource Conditions
Journal:
arXiv
Published Date:
Dec 30, 2024
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.
