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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.


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corrmtxData matrix for autocorrelation matrix estimation
levinsonLevinson-Durbin recursion
lpcLinear prediction filter coefficients
rlevinsonReverse Levinson-Durbin recursion
schurrcCompute reflection coefficients from autocorrelation sequence
ac2polyConvert autocorrelation sequence to prediction polynomial
ac2rcConvert autocorrelation sequence to reflection coefficients
is2rcConvert inverse sine parameters to reflection coefficients
lar2rcConvert log area ratio parameters to reflection coefficients
lsf2polyConvert line spectral frequencies to prediction filter coefficients
poly2acConvert prediction filter polynomial to autocorrelation sequence
poly2lsfConvert prediction filter coefficients to line spectral frequencies
poly2rcConvert prediction filter polynomial to reflection coefficients
rc2acConvert reflection coefficients to autocorrelation sequence
rc2isConvert reflection coefficients to inverse sine parameters
rc2larConvert reflection coefficients to log area ratio parameters
rc2polyConvert reflection coefficients to prediction filter polynomial
arburgAutoregressive all-pole model parameters — Burg’s method
arcovAutoregressive all-pole model parameters — covariance method
armcovAutoregressive all-pole model parameters — modified covariance method
aryuleAutoregressive all-pole model parameters — Yule-Walker method
invfreqsIdentify continuous-time filter parameters from frequency response data
invfreqzIdentify discrete-time filter parameters from frequency response data
pronyProny method for filter design
stmcbCompute linear model using Steiglitz-McBride iteration


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