Papers › Rethinking CNN Models for Audio Classification

Rethinking CNN Models for Audio Classification

22 Jul 2020arXiv:2007.11154archive 2025-07-28

Kamalesh Palanisamy, Dipika Singhania, Angela Yao

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. Besides, we show that even though we use the pretrained model weights for initialization, there is variance in performance in various output runs of the same model. This variance in performance is due to the random initialization of linear classification layer and random mini-batch orderings in multiple runs. This brings significant diversity to build stronger ensemble models with an overall improvement in accuracy. An ensemble of ImageNet pretrained DenseNet achieves 92.89% validation accuracy on the ESC-50 dataset and 87.42% validation accuracy on the UrbanSound8K dataset which is the current state-of-the-art on both of these datasets.

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kamalesh0406/Audio-Classification officialmentioned in papermentioned on GitHubpytorch report

HeleneFabia/keyword-spotter mentioned on GitHubpytorch report

shijing001/unicertainty_calibration_audio_classifiers mentioned on GitHubpytorch report

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Tasks

Audio Classification Classification Environmental Sound Classification General Classification Music Information Retrieval Transfer Learning

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Methods

1x1 Convolution Average Pooling Batch Normalization Concatenated Skip Connection Convolution Dense Block Dense Connections Dropout Global Average Pooling Kaiming Initialization Max Pooling ReLU Softmax

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