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IEEE SIGNAL PROCESSING LETTERS, ACCEPTED …
IEEE SIGNAL PROCESSING LETTERS, ACCEPTED NOVEMBER 20161 Deep Convolutional Neural Networks and DataAugmentation for environmental SoundClassificationJustin Salamon and Juan Pablo BelloAbstract The ability of deep convolutional neural networks(CNN) to learn discriminative spectro-temporal patterns makesthem well suited to environmental sound classification. However,the relative scarcity of labeled data has impeded the exploitationof this family of high-capacity models. This study has twoprimary contributions: first, we propose a deep convolutionalneural network architecture for environmental sound classifica-tion. Second, we propose the use of audio data augmentation forovercoming the problem of data scarcity and explore the influenceof different augmentations on the performance of the proposedCNN architecture.
IEEE SIGNAL PROCESSING LETTERS, ACCEPTED NOVEMBER 2016 1 Deep Convolutional Neural Networks and Data Augmentation for Environmental Sound Classification
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