In the natural environment,non-stationary background noise affects the animal sound recognition directly.Given this problem,a new technology of animal sound recognition based on energy-frequency(E-F)feature is propose...In the natural environment,non-stationary background noise affects the animal sound recognition directly.Given this problem,a new technology of animal sound recognition based on energy-frequency(E-F)feature is proposed in this paper.The animal sound is turned into spectrogram to show the energy,time and frequency characteristics.The sub-band frequency division and sub-band energy division are carried out on the spectrogram for extracting the statistical characteristic of energy and frequency,so as to achieve sub-band power distribution(SPD)and sub-band division.Radon transform(RT)and discrete wavelet transform(DWT)are employed to obtain the important projection coefficients,and the energy values of sub-band frequencies are calculated to extract the sub-band frequency feature.The E-F feature is formed by combining the SPD feature and sub-band energy value feature.The classification is achieved by support vector machine(SVM)classifier.The experimental results show that the method can achieve better recognition effect even when the SNR is below10 dB.展开更多
基金Supported by the National Natural Science Foundation of China(No.61075022)
文摘In the natural environment,non-stationary background noise affects the animal sound recognition directly.Given this problem,a new technology of animal sound recognition based on energy-frequency(E-F)feature is proposed in this paper.The animal sound is turned into spectrogram to show the energy,time and frequency characteristics.The sub-band frequency division and sub-band energy division are carried out on the spectrogram for extracting the statistical characteristic of energy and frequency,so as to achieve sub-band power distribution(SPD)and sub-band division.Radon transform(RT)and discrete wavelet transform(DWT)are employed to obtain the important projection coefficients,and the energy values of sub-band frequencies are calculated to extract the sub-band frequency feature.The E-F feature is formed by combining the SPD feature and sub-band energy value feature.The classification is achieved by support vector machine(SVM)classifier.The experimental results show that the method can achieve better recognition effect even when the SNR is below10 dB.
