| Author |
Message |
RAMU
Guest
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 Posted:
Tue Nov 29, 2005 5:17 pm Post subject:
Linear predictive coefficient computation |
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To find the Linear predictive coefficents all are using auto
correlation with levinson durbin equation. I want to know is there any
alternative method availble that still reduces the computational
complexity to find the LPC coefficients. |
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Nilnod
Guest
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| Posted:
Tue Nov 29, 2005 5:17 pm Post subject:
Re: Linear predictive coefficient computation |
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RAMU wrote:
| Quote: | To find the Linear predictive coefficents all are using auto
correlation with levinson durbin equation. I want to know is there any
alternative method availble that still reduces the computational
complexity to find the LPC coefficients.
|
Hello Ramu,
I guess this is stadardized to use Levinson-Durbin for LPC solution.
There may be Schur algorithm as follows that might give faster
computation.
--- Schur's recursion from 1917 is related to the Levinson-Durbin
method,
* but faster on parallel architectures; where Levinson-Durbin would
take time
* proportional to p * log(p), Schur only requires time proportional to
p. The
* GSM coder uses an integer version of the Schur recursion.
If you search in net you may find Schur recusion algorithm.
Rgs,
Santosh |
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Guest
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| Posted:
Wed Nov 30, 2005 5:16 pm Post subject:
Re: Linear predictive coefficient computation |
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RAMU wrote:
| Quote: | To find the Linear predictive coefficents all are using auto
correlation with levinson durbin equation. I want to know is there any
alternative method availble that still reduces the computational
complexity to find the LPC coefficients.
|
If you are willing to trade filter optimality with computation speed,
you can use an adaptive method and stop after a few iterations. You can
tune the adaption parameter for fast tracking. GAL LMS has the
advantage that the convergence rate is roughly independent of the
signal statistics (eigenspread).
I also remember reading about an adaptive method that used a cascade of
FIRs (instead of one big filter). The pro was extremely fast
convergence and low computational complexity. As usual, you pay for
that with accuracy. They suggested the use of cascade FIRs for fast and
good initial estimates for standard adaptive methods. Can't remember
the exact title of the paper.
As a last resort, you might want to look into an adaptive IIR.
Regards,
Andor |
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