Learning phase-invariant dictionaries

In this paper, we present a novel algorithm to learn phase-invariant dictionaries, which can be used to efficiently approximate a variety of signals, such as audio signals or images. Our approach relies on finding a small number of generating atoms that can be used-along with their phase-shifts-to sparsely approximate a given signal. Our method is inspired by the K-SVD algorithm, but imposes an extra constraint that the dictionaries we learn are phase-invariant. We show that the learned dictionaries achieve competitive approximation performance compared to that of state-of-the-art methods for audio signals and images, while substantially reducing the storage requirements and computational complexity.