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• W2994472917 abstract "In this paper, we present a deep learning framework applied for acoustic scene classification (ASC) recognizing the environmental sounds. Since an audio scene related to a given location potentially contains numerous sound events, only few of these events supply helpful information on the scene, which makes the acoustic scene classification task become a very complex problem. To confront this challenge, we suggest a novel architecture consisting of two basic processes. The front-end process approaches a spectrogram feature, using Gammatone filters. Regarding the back-end classification, we propose a novel convolutional neural network (CNN) architecture that enforces the network deeply learning middle convolutional layers. Our experiments conducted over DCASE2016 task 1A dataset offer the highest classification accuracy of 84.4% as compared to 72.5% of DCASE2016 baseline." @default.
• W2994472917 created "2019-12-13" @default.
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• W2994472917 date "2019-10-01" @default.
• W2994472917 modified "2023-10-16" @default.
• W2994472917 title "Acoustic Scene Classification Using A Deeper Training Method for Convolution Neural Network" @default.
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• W2994472917 doi "https://doi.org/10.1109/isee2.2019.8921365" @default.
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