Speech denoising based on a greedy adaptive dictionary algorithm

In this paper we consider the problem of speech denoising based on a greedy adaptive dictionary (GAD) algorithm. The transform is orthogonal by construction, and is found to give a sparse representation of the data being analysed, and to be robust to additive Gaussian noise. The performance of the algorithm is compared to that of the principal component analysis (PCA) method, for a speech denoising application. It is found that the GAD algorithm offers a sparser solution than PCA, while having a similar performance in the presence of noise.