Low-complexity deep learning frameworks for acoustic scene classification

• URL: http://arxiv.org/abs/2206.06057v1
• Date: Mon, 13 Jun 2022 11:41:39 GMT
• Title: Low-complexity deep learning frameworks for acoustic scene classification
• Authors: Lam Pham, Dat Ngo, Anahid Jalali, Alexander Schindler
• Abstract summary: We present low-complexity deep learning frameworks for acoustic scene classification (ASC) The proposed frameworks can be separated into four main steps: Front-end spectrogram extraction, online data augmentation, back-end classification, and late fusion of predicted probabilities. Our experiments conducted on DCASE 2022 Task 1 Development dataset have fullfiled the requirement of low-complexity and achieved the best classification accuracy of 60.1%.
• Score: 64.22762153453175
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this report, we presents low-complexity deep learning frameworks for acoustic scene classification (ASC). The proposed frameworks can be separated into four main steps: Front-end spectrogram extraction, online data augmentation, back-end classification, and late fusion of predicted probabilities. In particular, we initially transform audio recordings into Mel, Gammatone, and CQT spectrograms. Next, data augmentation methods of Random Cropping, Specaugment, and Mixup are then applied to generate augmented spectrograms before being fed into deep learning based classifiers. Finally, to achieve the best performance, we fuse probabilities which obtained from three individual classifiers, which are independently-trained with three type of spectrograms. Our experiments conducted on DCASE 2022 Task 1 Development dataset have fullfiled the requirement of low-complexity and achieved the best classification accuracy of 60.1%, improving DCASE baseline by 17.2%.

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