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.

Functions

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Linear Predictive Coding

`corrmtx`	Data matrix for autocorrelation matrix estimation
`levinson`	Levinson-Durbin recursion
`lpc`	Linear prediction filter coefficients
`rlevinson`	Reverse Levinson-Durbin recursion
`schurrc`	Compute reflection coefficients from autocorrelation sequence
`xcorr`	Cross-correlation
`xcov`	Cross-covariance
`ac2poly`	Convert autocorrelation sequence to prediction polynomial
`ac2rc`	Convert autocorrelation sequence to reflection coefficients
`is2rc`	Convert inverse sine parameters to reflection coefficients
`lar2rc`	Convert log area ratio parameters to reflection coefficients
`lsf2poly`	Convert line spectral frequencies to prediction filter coefficients
`poly2ac`	Convert prediction filter polynomial to autocorrelation sequence
`poly2lsf`	Convert prediction filter coefficients to line spectral frequencies
`poly2rc`	Convert prediction filter polynomial to reflection coefficients
`rc2ac`	Convert reflection coefficients to autocorrelation sequence
`rc2is`	Convert reflection coefficients to inverse sine parameters
`rc2lar`	Convert reflection coefficients to log area ratio parameters
`rc2poly`	Convert reflection coefficients to prediction filter polynomial

Parametric Modeling

`arburg`	Autoregressive all-pole model parameters — Burg’s method
`arcov`	Autoregressive all-pole model parameters — covariance method
`armcov`	Autoregressive all-pole model parameters — modified covariance method
`aryule`	Autoregressive all-pole model parameters — Yule-Walker method
`invfreqs`	Identify continuous-time filter parameters from frequency response data
`invfreqz`	Identify discrete-time filter parameters from frequency response data
`prony`	Prony method for filter design
`stmcb`	Compute linear model using Steiglitz-McBride iteration

Topics

Linear Prediction and Autoregressive Modeling
Compare two methods for determining the parameters of a linear filter: autoregressive modeling and linear prediction.
AR Order Selection with Partial Autocorrelation Sequence
Assess the order of an autoregressive model using the partial autocorrelation sequence.
Parametric Modeling
Study techniques that find the parameters for a mathematical model describing a signal, system, or process.
Prediction Polynomial
Obtain the prediction polynomial from an autocorrelation sequence. Verify that the resulting prediction polynomial has an inverse that produces a stable all-pole filter.

Featured Examples

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

Estimate vowel formant frequencies using linear predictive coding.

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AR Order Selection with Partial Autocorrelation Sequence

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

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