基于三维卷积神经网络的虫音特征识别方法

进口木材蛀虫检疫是海关的一项重要工作，但其存在着虫声检测算法准确率低、鲁棒性差等问题。针对这些问题，提出了一种基于三维卷积神经网络（3D CNN）的虫音检测方法以实现虫音特征的识别。首先，对原始虫音音频进行交叠分帧预处理，并使用短时傅里叶变换得到虫音音频的语谱图；然后，将语谱图作为3D CNN的输入，使其通过包含三层卷积层的3D CNN以判断音频中是否存在虫音特征。通过设置不同分帧长度下的输入进行网络训练及测试；最后以准确率、F₁分数以及ROC曲线作为评估指标进行性能分析。结果表明，在交叠分帧长度取5 s时，训练及测试效果最佳。此时，3D CNN模型在测试集上的准确率达到96.0%，F₁分数为0.96，且比二维卷积神经网络（2D CNN）模型准确率提高近18%。说明所提算法能准确地从音频信号中提取虫音特征并完成蛀虫识别任务，为海关检验检疫提供有力保障。

Insect sound feature recognition method based on three-dimensional convolutional neural network

The quarantine of imported wood is an important task for the customs, but there are problems such as low accuracy and poor robustness in the insect sound detection algorithm. To solve these problems, an insect sound detection method based on Three-Dimensional Convolutional Neural Network (3D CNN) was proposed to detect the presence of insect sound features. Firstly, the original insect audio was framed and pre-processed, and Short-Time Fourier Transform (STFT) was operated to obtain the spectrogram of the insect audio. Then, the spectrogram was used as the input of the 3D CNN consisting three convolutional layers. Network training and testing were conducted by setting inputs with different framing lengths. Finally, the analysis of performance was carried out using metrics like accuracy, F₁ score and ROC curve. The experiments showed that the test results were best when the overlap framing length was 5 seconds. The best result of the 3D CNN model on the test set achieved an accuracy of 96.0% and an F₁ score of 0.96. The accuracy was increased by nearly 18% compared with that of the two-dimensional convolutional neural network (2D CNN) model. It shows that the proposed model can extract the insect sound features from the audio signal more accurately and complete the insect identification task, which provides an engineering solution for customs inspection and quarantine.