Unveiling bias compensation in turbo-based algorithms for (discrete) compressed sensing
This work presents a series of sparse signal modeling algorithms implemented in a variable rate CELP coder in order to compare their performances at a reasonable computational load. Multipulse excitation (MPE), Multi-Pulse Maximum Likelihood Quantization (MP-MLQ), Algebraic CELP (ACELP) and hybrid excitation schemes are analyzed under a common framework. New approaches are proposed, based on cyclic and parallel use of fast greedy algorithms. These algorithms yield a statistically significant reduction of signal approximation error at a controllable computational complexity. Main results were confirmed by comparing MOS values obtained with the PESQ algorithm.