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- W3201727449 abstract "In the classification of birdsong, the effect of traditional feature extraction methods and identifying birds through deep learning methods is still not ideal. Therefore, this paper proposes a birdsong classification method based on Gabor_Wt feature image and convolutional neural network. According to the two-dimensional wavelet spectrogram signal can present more detailed information characteristics of birdsong. The two-dimensional wavelet spectrogram is obtained by using wavelet transform from birdsong audio. The Gabor_Wt feature images are extracted texture information from wavelet spectrogram with 2D-Gabor filters. With the convolutional neural network structure, WT_CNN and Gabor Wt Cnn classification models are built. The experimental results show that the Gabor_Wt_Cnn method can achieve a classification accuracy of 94%, outperforming WT_CNN of 87% and random forest with MFCC features of 80%. The proposed method provides a new reference to birdsong classification." @default.
- W3201727449 created "2021-10-11" @default.
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- W3201727449 date "2021-08-20" @default.
- W3201727449 modified "2023-09-24" @default.
- W3201727449 title "Classification Method of Birdsong Based on Gabor_WT Feature Image and Convolutional Neural Network" @default.
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- W3201727449 doi "https://doi.org/10.1109/prai53619.2021.9551079" @default.
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