Sparse denoising of audio by greedy time-frequency shrinkage

Matching Pursuit (MP) is a greedy algorithm that iteratively builds a sparse signal representation. This work presents an analysis of MP in the context of audio denoising. By interpreting the algorithm as a simple shrinkage approach, we identify the factors critical to its success, and propose several approaches to improve its performance and robustness. We present experimental results on a wide range of audio signals, and show that the method is able to yield results thats are competitive with other audio denosing approaches. Notably, the proposed approach retains a small percentage of the transform signal coefficients in building a denoised representation, i.e., it produces very sparse denoised results.