LINEAR PREDICTION MODELS Linear Prediction Coding Forward, Backward and Lattice Predictors Short-term and Long-Term Linear Predictors MAP Estimation of Predictor Coefficients Sub-Band Linear Prediction Signal Restoration Using Linear Prediction Models Summary inear prediction modelling is used in a diverse area of applications, such as data forecasting, speech coding, video coding, speech recognition, model-based spectral analysis, model-based interpolation, signal restoration, and impulse/step event detection. In the statistical literature, linear prediction models are often referred to as autoregressive (AR) processes. In this chapter, we introduce the theory of linear. | Advanced Digital Signal Processing and Noise Reduction Second Edition. Saeed V. Vaseghi Copyright 2000 John Wiley Sons Ltd ISBNs 0-471-62692-9 Hardback 0-470-84162-1 Electronic 8 LINEAR PREDICTION MODELS Linear Prediction Coding Forward Backward and Lattice Predictors Short-term and Long-Term Linear Predictors MAP Estimation of Predictor Coefficients Sub-Band Linear Prediction Signal Restoration Using Linear Prediction Models Summary Linear prediction modelling is used in a diverse area of applications such as data forecasting speech coding video coding speech recognition model-based spectral analysis model-based interpolation signal restoration and impulse step event detection. In the statistical literature linear prediction models are often referred to as autoregressive AR processes. In this chapter we introduce the theory of linear prediction modelling and consider efficient methods for the computation of predictor coefficients. We study the forward backward and lattice predictors and consider various methods for the formulation and calculation of predictor coefficients including the least square error and maximum a posteriori methods. For the modelling of signals with a quasi-periodic structure such as voiced speech an extended linear predictor that simultaneously utilizes the short and long-term correlation structures is introduced. We study sub-band linear predictors that are particularly useful for sub-band processing of noisy signals. Finally the application of linear prediction in enhancement of noisy speech is considered. Further applications of linear prediction models in this book are in Chapter 11 on the interpolation of a sequence of lost samples and in Chapters 12 and 13 on the detection and removal of impulsive noise and transient noise pulses. 228 Linear Prediction Models a x t .Pxxf f xx i kPxxf f b Figure The concentration or spread of power in frequency indicates the predictable or random character of a signal a a
