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- LPC question

Subject: | LPC question |

Posted by: | Jack (NOSP…@THANK.YOU) |

Date: | Mon, 15 May 2006 |

Hi,

I have read some litterature about linear prediction.

However, I haven't been able to find any litterature

that explains in mathematical terms exactly why

minimization of the variance of the residual leads

to an estimate of the all-pole filter coefficients.

The known output of a 10th order unknown all-pole filter is:

x[k]=u[k]-sum(a[q]x[k-q],q=1,q=10)

where the unknown u[k] is defined to be random noise with

variance 1 and the unknown, constants a[q] are the coefficients

of the all-pole filter.

Now...if I send x[k] through a FIR filter I get:

e[k]=x[k]+sum(b[q]x[k-q],q=1,q=10)

where the constants b[q] are the coefficients

of the FIR filter.

I can see that choosing b[q]=a[q] leads to

e[k]=u[k]

but I don't understand why minimizing the

variance of e[k] guarantees that

the resulting estimates of b[q] are

close to a[q]

How do I prove that mathematically?

I haven't been able to find such a proof

with google.

Maybe some of you guys could help me?

Thanks :o)

- Re: LPC question posted by Hanspi on 15 May 2006