为了提高电网渉鸟故障防治的针对性，提出了一种基于Mel频谱图和卷积神经网络（CNN）的鸟声识别方法。建立常见渉鸟故障对应的40类代表性鸟种的鸣声样本集，对鸟鸣信号进行分帧、加窗与降噪等预处理，计算每帧信号在各个Mel滤波器中的能量，根据能量大小与颜色深浅的映射关系提取鸟鸣信号的Mel频谱图。以电网涉鸟故障相关鸟种的Mel频谱图作为输入，通过CNN反复执行卷积-池化过程提取Mel频谱图特征，并进行多次迭代训练调整网络内部参数，得到最优模型用于鸟种识别。算例结果表明，40类鸟种的识别准确率达96.1%，识别效果优于其他迁移学习模型。文中研究结果可为输电线路运维人员正确识别相关鸟种、开展渉鸟故障差异化防治提供参考。

In order to achieve targeted prevention of bird-related outages in power grid, this paper proposed a bird sound recognition method based on Mel spectrogram and convolutional neural network (CNN). The study established a sound signal sample set of 40 kinds of representative bird species related to the common faults and preprocessed the bird sound signals by framing, windowing, and denoising, thus to calculate the signal energy of each frame in each Mel filter. The Mel spectrograms of bird sound signals were extracted according to the mapping relationship between the energy and the color depth. Moreover, the Mel spectrograms of the bird species related to po-wer grid faults were used as inputs, and the CNN was used for feature extraction of the Mel spectrograms by perfor-ming the convolution-pooling process repeatedly. Several iterations of training were carried out to adjust the internal parameters of the network, thus to obtain the optimal model for bird recognition. A case study results show that the recognition accuracy of 40 bird species reaches 96.1%, and that the proposed method is better than other transfer learning models. This study can provide references for transmission line operators to identify the related birds correctly and carry out differential prevention of bird-related outages.