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In this paper we argue that the Wigner-Ville distribution (WVD), instead of the spectrogram, should be used as basic input into convolutional neural network (CNN) based classification schemes. The WVD has superior resolution and localization as compared to other time-frequency representations. We present a method where a large-size kernel may be learned from the data, to enhance features important...
A novel feature extraction method for low-dimensional signal representation is presented. The features are useful for classification of non-stationary multi-component signals with stochastic variation in amplitudes and time-frequency locations. Using a penalty function to suppress the Wigner-Ville ambiguity function auto-terms, the proposed feature set is based on the cross-term doppler- and lag profiles...
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