基于深度学习声谱图分类的"听声识风"

将深度学习与声谱图相结合，提出了一种新型的风级识别方法——"听声识风". 在实验室条件下模拟1~4级风并记录对应风声音频. 通过傅里叶变换等方法将风声音频转换成声谱图，共得到2 608幅二维声谱图像用作数据集. 将声谱图数据集导入深度卷积神经网络GoogLeNet中进行风力等级识别，测试准确率达到了99.6%. 为了进一步证明实验结果的可靠性，将声谱图数据集分别导入ResNet18、ShuffleNet中进行训练，均获得了99.2%的测试准确率，结果表明该方法可以有效地进行风级识别. "听声识风"研究首次通过深度学习声谱图分类实现了对风级的识别，这是一种智能的、快速的风级识别新方法.

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

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.