Analysis of the smoothed residual-driven algorithm for speech coders

The smoothed residual-driven (SRD) algorithm was devised recently to adapt the short-term predictor coefficients in low-delay speech coders. This algorithm uses a smoothed version of the prediction residual for coefficient adaptation to provide good-quality speech while maintaining robustness to channel errors. The SRD algorithm is further investigated here to understand the effect of the smoothing approximation on the algorithm tracking capability for tone, AR, and speech inputs in the presence of channel errors. The transversal structure SRD algorithm is studied here. The relationship between the minimum MSE values with the SRD and signal-driven (SD) algorithms is derived as a function of the predictor coefficients. Tracking analysis shows that the SRD algorithm can track the input if the step size μ is sufficiently small. The LMS-SRD algorithm is stable and succeeds in tracking all inputs. The LMS-SRD algorithm is compared with other existing algorithms such as the SD, residual-driven (RD), and LMS-CCITT algorithms