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Based on the transfer learning, we design a bird species identification modelthat uses the VGG-16 model (pretrained on ImageNet) for feature extraction,then a classifier consisting of two fully-connected hidden layers and a Softmaxlayer is attached. We compare the performance of the proposed model with theoriginal VGG16 model. The results show that the former has higher trainefficiency, but lower mean average precisions(MAP). To improve the MAP of theproposed model, we investigate the result fusion mode to form multi-channelidentification model, the best MAP reaches 0.9998. The number of modelparameters is 13110, which is only 0.0082% of the VGG16 model. Also, the sizedemand of sample is decreased.

Audio-only Bird Species Automated Identification Method with Limited Training Data Based on Multi-Channel Deep Convolutional Neural Networks