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《Journal of Applied Acoustics》 2020-03
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Research on the application of energy spectrum with voiceprint information in bird recognition

YANG Chunyong;QI Hongda;PENG Yanqiu;YIN Bin;HOU Jin;SHU Zhenyu;CHEN Shaoping;Hubei Key Laboratory of Intelligent Wireless Communications;College of Electronics and Information Engineering, South-Central University for Nationalities;  
The bird's voice recognition technology combined with the Mel-frequency cepstral coefficients and the Gaussian mixture model(MFCC+GMM) method is difficult to adapt to the noise environment, and its computational complexity is high. In this paper, a novel bird recognition method using voice-power spectrum(VPS-BR) to express acoustic features is proposed. It utilizes the multi-dimensional difference of bird sounds on the power spectrum to quantitatively identify the texture features of the sound. In the feature extraction step,the edge texture of the bird's voice-power spectrum is characterized by local binary pattern(LBP) and direction gradient histogram(HOG); in the identification step, the VPS-BR model is constructed by combining LBP and HOG with support vector machine, K nearest neighbor(KNN) and random forest. The cross-validation of 15 original noisy bird sound data sets from the Xeno-Canto website shows that the recognition rate of the VPS-BR model is better than the MFCC+GMM model; HOG and KNN combined model recognition rate can reach 90.5%, shows good noise-reception recognition performance. Finally, for the lack of sample data set, image enhancement is made by using generated-adversarial-network, and the recognition rate is further increased by 1.48%.
【CateGory Index】: Q958;TN912.34
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