Enhanced Signal Matching Pursuit Sparse Decomposition Code

This paper presents an enhanced signal matching pursuit sparse decomposition code utilizing Gabor time-frequency atoms as fundamental basis functions. The implementation employs an iterative greedy algorithm that selects optimal atoms from a redundant dictionary through correlation maximization, significantly improving speech signal reconstruction quality and clarity. Key enhancements include optimized atom selection criteria using threshold-based matching and residual energy minimization, along with efficient orthogonal projection operations for coefficient updates. The improved code features advanced signal characterization capabilities through multi-resolution time-frequency analysis and implements adaptive stopping conditions based on reconstruction error thresholds. These algorithmic refinements enable more accurate capture of signal features and efficient extraction of critical information components. The solution demonstrates substantial potential for speech signal processing and analysis applications, with robust performance across various implementation scenarios including noise reduction, feature extraction, and compressed sensing. The MATLAB-based implementation incorporates optimized matrix operations for dictionary generation and fast convolution techniques for real-time processing capabilities.