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Convert linear prediction coefficients to cepstral coefficients or cepstral coefficients to linear prediction coefficients

Estimation / Linear Prediction

`dsplp`

The LPC to/from Cepstral Coefficients block either converts
linear prediction coefficients (LPCs) to cepstral coefficients (CCs)
or cepstral coefficients to linear prediction coefficients. Set the **Type
of conversion** parameter to ```
LPCs to cepstral
coefficients
```

or ```
Cepstral coefficients to
LPCs
```

to select the domain into which you want to convert
your coefficients. The LPC port corresponds to LPCs, and the CC port
corresponds to the CCs. For more information, see Algorithm.

The block input can be an * N*-by-

Consider a signal * x*(

When you select `LPCs to cepstral coefficients`

from
the **Type of conversion** list, you can specify
the prediction error power in two ways. From the **Specify
P** list, choose `via input port`

to
input the prediction error power using input port P. The input to
the port must be a vector with length equal to the number of input
channels. Select `assume P equals 1`

to set
the prediction error power equal to 1 for all channels.

When you select `LPCs to cepstral coefficients`

from
the **Type of conversion** list, the **Output
size same as input size** check box appears. When you select
this check box, the length of the input vector of LPCs is equal to
the output vector of CCs. When you do not select this check box, enter
a positive scalar for the **Length of output cepstral coefficients** parameter.

When you select `LPCs to cepstral coefficients`

from
the **Type of conversion** list, you can use
the **If first input value is not 1 **parameter to
specify the behavior of the block when the first coefficient of the
LPC vector is not 1. The following options are available:

`Replace it with 1`

—- Changes the first value of the coefficient vector to 1. The other coefficient values are unchanged.`Normalize`

— Divides the entire vector of coefficients by the first coefficient so that the first coefficient of the LPC vector is 1.`Normalize and Warn`

— Divides the entire vector of coefficients by the first coefficient so that the first coefficient of the LPC vector is 1. The block displays a warning message telling you that your vector of coefficients has been normalized.`Error`

— Displays an error telling you that the first coefficient of the LPC vector is not 1.

When you select `Cepstral coefficients to LPCs`

from
the **Type of conversion** list, the **Output
P** check box appears on the block. Select this check box
when you want to output the prediction error power from output port
P.

The cepstral coefficients are the coefficients of the Fourier
transform representation of the logarithm magnitude spectrum. Consider
a sequence, * x*(

When in this mode, this block uses a recursion technique to convert LPCs to CCs. The LPC vector is defined by $$\left[\begin{array}{cccc}{a}_{0}& {a}_{1}& {a}_{2}& \begin{array}{cc}\mathrm{...}& {a}_{p}\end{array}\end{array}\right]$$ and the CC vector is defined by $$\left[\begin{array}{ccccccc}{c}_{0}& {c}_{1}& {c}_{2}& \mathrm{...}& {c}_{p}& \mathrm{...}& {c}_{n-1}\end{array}\right]$$. The recursion is defined by the following equations:

$${c}_{0}={\mathrm{log}}_{e}P$$

$${c}_{m}=-{a}_{m}+\frac{1}{m}{\displaystyle \sum _{k=1}^{m-1}\left[-\left(m-k\right)\cdot {a}_{k}\cdot {c}_{\left(m-k\right)}\right]},1\le m\le p$$

$${c}_{m}={\displaystyle \sum _{k=1}^{p}\left[\frac{-\left(m-k\right)}{m}\cdot {a}_{k}\cdot {c}_{\left(m-k\right)}\right]},p<m<n$$

When in this mode, this block uses a recursion technique to convert CCs to LPCs. The CC vector is defined by $$\left[\begin{array}{cccc}\begin{array}{cccc}{c}_{0}& {c}_{1}& {c}_{2}& \mathrm{...}\end{array}& {c}_{p}& \mathrm{...}& {c}_{n}\end{array}\right]$$ and the LPC vector is defined by $$\left[\begin{array}{cccc}{a}_{0}& {a}_{1}& {a}_{2}& \begin{array}{cc}\mathrm{...}& {a}_{p}\end{array}\end{array}\right]$$. The recursion is defined by the following equations

$${a}_{m}=-{c}_{m}-\frac{1}{m}{\displaystyle \sum _{k=1}^{m-1}\left[\left(m-k\right)\cdot {c}_{\left(m-k\right)}\cdot {a}_{k}\right]}$$

$$P=\mathrm{exp}\left({C}_{0}\right)$$

where $$m=1,2,\mathrm{...},p$$.

**Type of conversion**Choose

`LPCs to cepstral coefficients`

or`Cepstral coefficients to LPCs`

to specify the domain into which you want to convert your coefficients.**Specify P**Choose

`via input port`

to input the values of prediction error power using input port P. Select`assume P equals 1`

to set the prediction error power equal to 1.**Output size same as input size**When you select this check box, the length of the input vector of LPCs is equal to the output vector of CCs.

**Length of output cepstral coefficients**Enter a positive scalar that is the length of each output channel of CCs.

**If first input value is not 1**Select what you would like the block to do when the first coefficient of the LPC vector is not 1. You can choose

`Replace it with 1`

,`Normalize`

,`Normalize and Warn`

, and`Error`

.**Output P**Select this check box to output the prediction error power for each channel from output port P.

Papamichalis, Panos E. *Practical Approaches to Speech
Coding.* Englewood Cliffs, NJ: Prentice Hall, 1987.

Double-precision floating point

Single-precision floating point

Levinson-Durbin | DSP System Toolbox |

LPC to LSF/LSP Conversion | DSP System Toolbox |

LSF/LSP to LPC Conversion | DSP System Toolbox |

LPC to/from RC | DSP System Toolbox |

LPC/RC to Autocorrelation | DSP System Toolbox |

Real Cepstrum | DSP System Toolbox |

Complex Cepstrum | DSP System Toolbox |

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