Robin Patrick Decker wrote:
I would really appriciate an explanation or information on a good
resource to learn more about how the prediction coefficients are solved
for.
The Wikipedia page on this subject is not terrible:
https://en.wikipedia.org/wiki/Linear_prediction
The very high-level answer is that if want to choose your coefficients
to minimize the mean squared error of the prediction, then you get a
least-squares problem where the matrix you're inverting is just the
auto-correlation matrix of your signal (the Yule-Walker equations). The
discrete Fourier transform is just a computationally efficient means of
computing the auto-correlation, at least if your order is sufficiently high.
Once the lpc coefficients have been solved, as far as I understand you
must also store part of original signal with length equal to the
prediction order since you need the previous samples to predict the
next sample of which only the residual is known. For the next values
the residual is used to retrieve the original value which is fed into
the predction model to further reconstruct the time series. Is this
correct?
Yes.
_______________________________________________
flac-dev mailing list
flac-dev@xiph.org
http://lists.xiph.org/mailman/listinfo/flac-dev
