Assessing Compressive Sensing Methods for Impulsive Noise Reduction in Audio Signals

Impulsive noise often corrupts audio signals due to various factors, leading to a degradation in signal quality and clarity. In this paper, we analyze denoising techniques based on compressive sensing (CS) theory. CS-based methods leverage signal sparsity or high concentration in specific transformation domains. Audio signals exhibit high concentration in the discrete cosine transform (DCT) domain. CS-based denoising involves two main steps: (i) detecting corrupted samples and (ii) reconstructing samples at detected positions. The positions of the noisy samples identified in the first step are considered inaccessible, and we employ compressive sensing methods to reconstruct these values. We will evaluate the performance of two CS methods alongside a traditional denoising approach based on the median filter.