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• W2884142018 abstract "In time series classification, signals are typically mapped into some intermediate representation which is used to construct models. We introduce the joint time-frequency scattering transform, a locally time-shift invariant representation which characterizes the multiscale energy distribution of a signal in time and frequency. It is computed through wavelet convolutions and modulus non-linearities and may therefore be implemented as a deep convolutional neural network whose filters are not learned but calculated from wavelets. We consider the progression from mel-spectrograms to time scattering and joint time-frequency scattering transforms, illustrating the relationship between increased discriminability and refinements of convolutional network architectures. The suitability of the joint time-frequency scattering transform for characterizing time series is demonstrated through applications to chirp signals and audio synthesis experiments. The proposed transform also obtains state-of-the-art results on several audio classification tasks, outperforming time scattering transforms and achieving accuracies comparable to those of fully learned networks." @default.
• W2884142018 created "2018-08-03" @default.
• W2884142018 creator A5027789239 @default.
• W2884142018 creator A5043290570 @default.
• W2884142018 creator A5065086376 @default.
• W2884142018 date "2018-07-24" @default.
• W2884142018 modified "2023-09-27" @default.
• W2884142018 title "Classification with Joint Time-Frequency Scattering." @default.
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