Timeline for Gentle request for explanation on LPC and LPCC coefficients :)

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Dec 9, 2013 at 12:40	comment	added	Kenneide		The (usual) way that linear prediction determines the set of coefficients is by minimizing $\sum^{N-1}_{n=0} e[n]^2$ -- that is to say, by minimizing the error signal energy over a range of samples. For some points, $\|e[n]\|$ will be very close to 0, for others it will be larger, but, overall, the coefficients gives the lowest energy. The original signal $x[n]$ can be regenerated given $e[n]$ and $a_k$, i.e. by filtering $e[n]$ with the all-pole filter described by $a_k$.
Dec 9, 2013 at 10:52	comment	added	Celdor		Ty. I still find it hard to see the advantage of LPCs. We've a sample x[n] and we generate additional coefficients to predict this sample. For the very next sample, we generate another $p$ coefficients, etc. This produces extra information. I could say that in frequency domain we can represent a signal with $p$ all-pole coefficients, as you said. Is it the same when I say that $N$-length signal is represented by $p$-length predicted signal with error e[n] in frequency? I am sorry, I am really confused. There are details I can see and understand but still don't find it useful.
Dec 9, 2013 at 9:58	history	answered	Kenneide	CC BY-SA 3.0