Developing a signal compression technique that is able to achieve a low bit rate while
maintaining high perceptual signal quality is a classical signal processing problem …

In speech processing applications, imposing sparsity constraints on high-order linear
prediction coefficients and prediction residuals has proven successful in overcoming some …

In linear prediction of speech, the 1-norm error minimization criterion has been shown to
provide a valid alternative to the 2-norm minimization criterion. However, unlike 2-norm …

Linear Prediction (LP) analysis is speech analysis to estimate AR (Auto-Regressive)
coefficients to represent the all-pole spectrum that is applied in speech synthesis recently …

Linear Prediction (LP) analysis is commonly used in speech processing. LP is based on
Auto-Regressive (AR) model and it estimates the AR model parameter from signals with l 2 …

Spectral estimation performance determines that of speech processing. Linear Prediction
(LP) is the most successful speech analysis method commonly introduced worldwide of a …

Linear Prediction (LP) used in the CELP speech coding is the most successful speech
analysis method. However, it has drawbacks, such as low estimation accuracy due to its …

LP (Linear Prediction) analysis is the most commonly used and successful speech analysis
implemented in smartphones. We have been proposing time-varying complex AR (TV-CAR) …

Using a sparsity promoting convex penalty function on high-order linear prediction
coefficients and residuals has shown to result in improved modeling of speech and other …

Using a sparsity promoting convex penalty function on high-order linear prediction
coefficients and residuals addresses some inherent limitations of standard linear prediction …