DS-FlexiNet achieves 58.25% accuracy after int8 quantization on TAU22 Task 1A with 30.69K parameters and 8.27M MACs, using residual normalization, ADIR augmentation, and 12-teacher knowledge distillation.
Receptive field regu- larization techniques for audio classification and tagging with deep convolutional neural networks,
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Improving Acoustic Scene Classification in Low-Resource Conditions
DS-FlexiNet achieves 58.25% accuracy after int8 quantization on TAU22 Task 1A with 30.69K parameters and 8.27M MACs, using residual normalization, ADIR augmentation, and 12-teacher knowledge distillation.