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《Journal of Applied Acoustics》 2020-02
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Bird species recognition method based on multi-feature fusion

XIE Jiangjian;YANG Jun;XING Zhaoliang;ZHANG Zhuo;CHEN Xin;School of Technology, Beijing Forestry University;Key Lab of National Forestry and Grassland Administration for Forestry Equipment and Automation;State Key Laboratory of Advanced Transmission Technology, Global Energy Interconnection Research Institute Co.Ltd.;  
The choice of input feature directly affects the classification performance of the deep learning, a multi-feature fusion recognition method was proposed to improve the classification performance of the bird species recognition model. In this method, firstly three kinds of spectrogram samples of vocalization signals were calculated through short time Fourier transform, Mel-frequency cepstrum transform and Chirplet transform respectively, then three single feature models which based on VGG16 transfer learning were trained using these three kinds of spectrogram samples accordingly, modified weighted cross entropy function was used to fix the problem of imbalanced data set, the outputs of three models were fused to classify the spectrograms and realize the recognition of bird species. Taken the 35 kinds of bird in ICML4 B database for study subject, the MAPs were compared, results show that the mean average precision(MAP) of feature fusion model is highest increased by 0.307 contrast to the single feature model; Three spectrogram durations, 100 ms, 300 ms and 500 ms were chosen to compare the test MAP of four models, the results reveal that the 300 ms duration is the best; the precision of 4 models with different SNR were compared, the precision reduction of feature fusion model as the SNR decreased is the least. The proposed model can achieve better performance with suitable duration, have anti-noise ability in some degree, and the trainable parameters are less, which is more suitable for birds with little samples.
【Fund】: 国家自然科学基金资助项目(31670553);; 国家电网公司科技项目(SGGR0000WLJS1801082);; 国家重点研发项目(2017YFC1403503);; 中央高校基本科研业务费专项(2016ZCQ08)
【CateGory Index】: Q959;TP391.41
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