OMP Program for Speech Compressed Sensing Reconstruction Algorithm

In this article, we introduce the Orthogonal Matching Pursuit (OMP) algorithm specifically applied to speech compressed sensing reconstruction. The core objective of this algorithm is to achieve efficient compression and reconstruction of speech signals by leveraging the OMP method's ability to recover sparse signals from limited measurements. The implementation typically involves iteratively selecting the most correlated atoms from a dictionary matrix to approximate the original signal. Key computational steps include: 1. Residual initialization with the original measurement vector 2. Iterative atom selection based on maximum correlation 3. Least-squares solution update for selected support set 4. Residual recalculation until convergence criteria are met We provide this algorithm to facilitate improved speech compression sensing experiences, offering advantages in computational efficiency and reconstruction quality compared to traditional methods. The code structure generally utilizes matrix operations for correlation calculations and includes threshold parameters for controlling reconstruction accuracy and sparsity constraints.