Shallow Convolutional Neural Networks for Acoustic Scene Classification

Recently, deep neural networks, which include convolutional neural networks（CNNs）, have been widely applied to acoustic scene classification（ASC）. Motivated by the fact that some simplified CNNs have shown improvements over deep CNNs, such as Visual Geometry Group Net（VGG-Net）, we have figured out h...

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Bibliographic Details
Published in:Wuhan University journal of natural sciences 2018-04, Vol.23 (2), p.178-184
Main Authors: Lu, Lu, Yang, Yuhong, Jiang, Yuzhi, Ai, Haojun, Tu, Weiping
Format: Article
Language:English
Subjects:
Acoustics
Artificial neural networks
Biomedical and Life Sciences
Classification
Computer Science
Life Sciences
Materials Science
Neural networks
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Summary:Recently, deep neural networks, which include convolutional neural networks（CNNs）, have been widely applied to acoustic scene classification（ASC）. Motivated by the fact that some simplified CNNs have shown improvements over deep CNNs, such as Visual Geometry Group Net（VGG-Net）, we have figured out how to simplify the VGG-Net style architecture to a shallow CNN with improved performance. Max pooling and batch normalization are also applied for better accuracy. With a series of controlled tests on detection and classification of acoustic scenes and events（DCASE） 2016 data sets, our shallow CNN achieves 6.7% improvement, and reduces time complexity to 5%, compared with the VGG-Net style CNN.
ISSN:1007-1202
1993-4998
DOI:10.1007/s11859-018-1308-z