Multi-Resolution Dual-Tree Wavelet Scattering Network for Signal Classification
This work addresses signal classification problems, likely for domains like audio or image processing, but appears incremental as it builds upon existing scattering network methods.
The paper tackled signal classification by introducing a Deep Scattering network using Dual-Tree complex wavelets to extract translation invariant representations, and it outperformed Mallat's ScatterNet on four datasets with different modalities in classification accuracy.
This paper introduces a Deep Scattering network that utilizes Dual-Tree complex wavelets to extract translation invariant representations from an input signal. The computationally efficient Dual-Tree wavelets decompose the input signal into densely 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 in the densely spaced, locally smooth, signal representations aids the learning of the classifier. The proposed network is shown to outperform Mallat's ScatterNet on four datasets with different modalities on classification accuracy.