Rethinking CNN Models for Audio Classification

Understand

In this paper, we show that ImageNet-Pretrained standard deep CNN models can be used as strong baseline networks for audio classification.

• Even though there is a significant difference between audio Spectrogram and standard ImageNet image samples, transfer learning assumptions still hold firmly.
• To understand what enables the ImageNet pretrained models to learn useful audio representations, we systematically study how much of pretrained weights is useful for learning spectrograms.
• We show (1) that for a given standard model using pretrained weights is better than using randomly initialized weights (2) qualitative results of what the CNNs learn from the spectrograms by visualizing the gradients.