Real-Time Vehicle Sound Detection System Based on Depthwise Separable Convolution Neural Network and Spectrogram Augmentation

被引:5
|
作者
Wang, Chaoyi [1 ,2 ]
Song, Yaozhe [1 ,2 ]
Liu, Haolong [1 ,2 ]
Liu, Huawei [1 ,2 ]
Liu, Jianpo [1 ,2 ]
Li, Baoqing [1 ,2 ]
Yuan, Xiaobing [1 ,2 ]
机构
[1] Chinese Acad Sci, Shanghai Inst Microsyst & Informat Technol, Sci & Technol Microsyst Lab, Shanghai 201899, Peoples R China
[2] 1455 Pingcheng Rd, Shanghai 201899, Peoples R China
关键词
depthwise separable convolutional neural networks; spectrogram augmentation; sound detection; vehicle detection;
D O I
10.3390/rs14194848
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
This paper proposes a lightweight model combined with data augmentation for vehicle detection in an intelligent sensor system. Vehicle detection can be considered as a binary classification problem, vehicle or non-vehicle. Deep neural networks have shown high accuracy in audio classification, and convolution neural networks are widely used for audio feature extraction and audio classification. However, the performance of deep neural networks is highly dependent on the availability of large quantities of training data. Recordings such as tracked vehicles are limited, and data augmentation techniques can be applied to improve the overall detection accuracy. In our case, spectrogram augmentation is applied on the mel spectrogram before extracting the Mel-scale Frequency Cepstral Coefficients (MFCC) features to improve the robustness of the system. Then depthwise separable convolution is applied to the CNN network for model compression and migrated to the hardware platform of the intelligent sensor system. The proposed approach is evaluated on a dataset recorded in the field using intelligent sensor systems with microphones. The final frame-level accuracy achieved was 94.64% for the test recordings and 34% of the parameters were reduced after compression.
引用
收藏
页数:13
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