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Signal Modeling

Linear prediction, autoregressive (AR) models, Yule-Walker, Levinson-Durbin

Signal Processing Toolbox™ provides parametric modeling techniques that let you estimate a rational transfer function that describes a signal, system, or process. Use known information about a signal to find the coefficients of a linear system that models it. Approximate a given time-domain impulse response using Prony and Steiglitz-McBride ARX models. Find an analog or digital transfer function that matches a given complex frequency response. Model resonances using linear prediction filters.

Parametric Modeling
Burg and Yule-Walker autoregressive models, Prony’s method
Linear Predictive Coding
LPC, Levinson-Durbin recursion

Featured Examples

Linear Prediction and Autoregressive Modeling

Linear Prediction and Autoregressive Modeling

Compare two methods for determining the parameters of a linear filter: autoregressive modeling and linear prediction.

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Formant Estimation with LPC Coefficients

Formant Estimation with LPC Coefficients

Estimate vowel formant frequencies using linear predictive coding.

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基于“增大化现实”技术的顺序选择部分自相关quence

基于“增大化现实”技术的顺序选择部分自相关quence

Assess the order of an autoregressive model using the partial autocorrelation sequence.

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Signal Processing Toolbox Documentation

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