北京林业大学学报2018，Vol.40Issue(3)：122-127,6.DOI:10.13332/j.1000-1522.20180008
基于Chirplet语图特征和深度学习的鸟类物种识别方法
Bird species recognition method based on Chirplet spectrogram feature and deep learning
摘要
Abstract
[ Objective ] The application of deep learning in bird species recognition is the research hotspot at present. To improve the performance of recognition, a bird species recognition method based on Chirplet spectrogram feature and VGG16 model was proposed. [ Method] Acoustic signal spectrograms were calculated by the Chirplet transform firstly, then spectrograms were inputted in the VGG16 model to realize the recognition of bird species. Taking eighteen bird species in Beijing Songshan National Nature Reserve as examples, through Chirplet transform, Fourier transform and Mel cepstrum transform, three spectrogram sample sets were calculated respectively, then using three kinds of spectrogram sample sets to train the recognition model, the performances of each input were compared. [ Result] Results showed that with the Chirplet diagram input, the highest mean average precision ( MAP ) of the test set was 0. 9871 compared with the other two inputs. Also, the epochs of the highest trainning MAP was the smallest. [ Conclusion ] The choice of input affects the classification performance of deep learning model. The vocalization zone of Chirplet spectrogram is more concentrate and obvious than STFT spectrogram and Mel spectrogram, which means Chirplet spectrogram is more suitable for the bird recognition based on VGG16 model, higher MAP and efficiency of recognition can be achieved.关键词
鸟类/线性调频小波变换/语图特征/深度卷积神经网络/物种识别Key words
bird/Chirplet transform/spectrogram feature/deep convolutional neural network/species recognition分类
信息技术与安全科学引用本文复制引用
谢将剑,李文彬,张军国,丁长青..基于Chirplet语图特征和深度学习的鸟类物种识别方法[J].北京林业大学学报,2018,40(3):122-127,6.基金项目
中央高校基本科研业务费专项(2017JC14)、国家重点研发项目(2017YFC1403503). （2017JC14）
