"Identify Wind Force by Listening" Based on Deep Learning Spectrogram Classification

YANG Haoyan; LUAN Tao; HAN Zhongzhi; NI Jiangong; GAO Jiyue

doi:10.6054/j.jscnun.2021069

Journal of South China Normal University (Natural Science Edition) > 2021 > 53(5): 10-16. > DOI: 10.6054/j.jscnun.2021069

YANG Haoyan, LUAN Tao, HAN Zhongzhi, NI Jiangong, GAO Jiyue. "Identify Wind Force by Listening" Based on Deep Learning Spectrogram Classification[J]. Journal of South China Normal University (Natural Science Edition), 2021, 53(5): 10-16. DOI: 10.6054/j.jscnun.2021069

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"Identify Wind Force by Listening" Based on Deep Learning Spectrogram Classification

1.
College of Animation and Communication, Qingdao Agricultural University, Qingdao 266109, China
2.
College of Science and Information Science, Qingdao Agricultural University, Qingdao 266109, China

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Received Date: April 11, 2021
Available Online: November 10, 2021

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Abstract

Abstract

Deep learning and spectrogram are innovatively combined to propose a new wind force level identification method, i.e., "identifying wind force by listening". Under laboratory conditions, the sound from 1~4 wind force was recorded as the original wind audio, which was converted into acoustic spectrograms with the Fourier transform and other methods, and a total of 2 608 two-dimensional acoustic spectrograms were obtained as network input data. The data were imported into the deep convolutional neural network GoogLeNet for wind force recognition, which reached an accuracy of 99.6%. In order to further verify the reliability of the experimental results, the spectrogram data were imported into ResNet18 and ShuffleNet for training, and the accuracy rate in both networks was 99.2%. The results showed that this method can effectively carry out wind force identification. "Identifying wind force by listening" realized wind force identification through deep learning for the first time, which is a new intelligent and fast wind force identification method.
- wind force identification,
- deep learning,
- GoogLeNet,
- spectrogram,
- wind sound

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