The activity and distribution of wild birds are biological indicators to evaluate biodiversity. In order to identify bird habitats, collecting and classifying sounds should have to do. Using the bird sound can make easier to distinguish location or type of wild birds. Recently, attempts to analyze bioacoustic data have been risen using the machine learning. We are going to classify the bird songs using deep learning. The bird songs convert into the spectrogram images. Spectrogram images are used for the input of convolutional neural network. In generally the bird song data set for classification contains a lot of noise. Even obtaining the data including noise is difficult. The data is about 200 bird sounds of 20 species. Based on transfer learning, ResNet34, ResNet50 and AlexNet of Convolutional Neural Network are used as the experiment. The experiment parameter is learning rate and epochs. As a result, the ResNet34 shows the highest accuracy of 99.7% and an average of 93% in the test. Therefore, In this paper, we are going to develop the deep learning system that classifies 20 kinds of bird song using ResNet34. By using this system, it can be helpful various activities such as the prevention of avian influenza.