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

Singh, A and Kingsbury, N (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.

Abstract

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.