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"Optimal" Predictor, help me understanding adaptive covariance estimation
post Nov 15 2006, 21:16
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As some of you may have noticed i'm also working on a lossless-audio-codec.
The core prediction uses a joint-channel cholesky decomposition every k-samples.
The decomposition is applied on a covariance matrix which is updated every sample in the following way

  for (int i=0;i<=maxorder;i++)
    for (int j=i;j<=maxorder;j++) covar[i][j] = decay * covar[i][j] + history[i] * history[j]

You can see there's a learning factor 'decay' involved. The problem is
that for some samples old-data seems to be more important than recent data
leading to a large encoding-cost if you use a relative high factor that works good
on most samples. I've tried to exploit some statistical properties as prediction gain
or variance of the input to estimate a optimal learning factor but this seems
to be more complicated than i first thought. Does anybody know of a good
way to estimate this property?

any help is appreciated


edit: typo in the codebox

This post has been edited by pest: Nov 16 2006, 14:13
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post Nov 15 2006, 22:21
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Joined: 20-March 04
From: Göttingen (DE)
Member No.: 12875

Looks like you want to do backward adaptive prediction. Are you sure you want a decoder to perform the same calculations (cholesky every k samples)? This is pretty time consuming, isn't it? Also, if you want your compressed files to be machine-independent you need to make sure, that the calculations you perform give the exact same results on every machine. (This is not the case if you rely on an FPU for these calculations.)

Backward adaptive prediction is not my specialty, sorry. Perhaps a sliding window approach helps.

This post has been edited by SebastianG: Nov 15 2006, 22:25
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