CUED Publications database

Multi-Resolution Dual-Tree Wavelet Scattering Network for Signal Classification

Singh, A and Kingsbury, NG (2016) Multi-Resolution Dual-Tree Wavelet Scattering Network for Signal Classification. In: 11th International Conference on Mathematics in Signal Processing, 2016-12-12 to 2016-12-14, Austin Court, Birmingham, UK.

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This paper introduces a Deep Scattering network that utilizes Dual-Tree complex wavelets to extract multi-scale translation invariant representations from an input signal. The computationally efficient Dual-Tree wavelets decompose the input signal into equally spaced representations over scales. Translation invariance is introduced in the representations by applying a non-linearity over a region followed by averaging. The discriminatory information from the equally spaced locally smooth signal representations aids the learning of the classi- fier. The proposed network is shown to outperform Mallat’s ScatterNet [1] on four datasets with different modalities, both for classification accuracy and computational efficiency.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Uncontrolled Keywords: DTCWT scattering network convolutional neural network USPS dataset UCI datasets
Divisions: Div F > Signal Processing and Communications
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 20:03
Last Modified: 05 Feb 2019 16:02