Urban acoustic classification based on deep feature transfer learning

Urban acoustic classification (UAC) plays a vital role in smart city engineering, urban security, noise pollution analysis, etc. Convolutional neural networks (CNNs) based feature transfer learning have been shown competitive performance in many applications but little attention has been paid to UAC...

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Bibliographic Details
Published in:Journal of the Franklin Institute 2020-01, Vol.357 (1), p.667-686
Main Authors: Shen, Yexin, Cao, Jiuwen, Wang, Jianzhong, Yang, Zhixin
Format: Article
Language:English
Subjects:
Acoustic noise
Acoustics
Algorithms
Artificial neural networks
Classification
Feature extraction
Machine learning
Neural networks
Noise pollution
Performance assessment
Smart cities
Stream pollution
Urban environments
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Summary:Urban acoustic classification (UAC) plays a vital role in smart city engineering, urban security, noise pollution analysis, etc. Convolutional neural networks (CNNs) based feature transfer learning have been shown competitive performance in many applications but little attention has been paid to UAC. In this study, a novel UAC algorithm exploiting the deep CNNs based feature transfer learning and the deep belief net (DBN) based classification is developed. The spectrogram is first adopted for the urban acoustic stream representation. Then, three deep CNNs pre-trained on ImageNet database are applied as feature extractors. The extracted features are concatenated and fed to a DBN for classifier learning. To achieve a good generalization performance, three restricted boltzmann machines (RBM) trained by the contrastive divergence algorithm (CD) followed by a back-propagation (BP) based fine parameter tuning is adopted in DBN. The proposed UAC is evaluated on a real acoustic database, including 11 categories of acoustic signals recorded from the urban environment. Performance comparisons to many state-of-the-art algorithms are presented to demonstrate the superiority of the proposed method.
ISSN:0016-0032
1879-2693
0016-0032
DOI:10.1016/j.jfranklin.2019.10.014