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Diffstat (limited to 'macros/ac2poly.sci~')
| -rw-r--r-- | macros/ac2poly.sci~ | 29 |
1 files changed, 0 insertions, 29 deletions
diff --git a/macros/ac2poly.sci~ b/macros/ac2poly.sci~ deleted file mode 100644 index 653ccce..0000000 --- a/macros/ac2poly.sci~ +++ /dev/null @@ -1,29 +0,0 @@ -function [a,varargout] = ac2poly(r) -// Convert autocorrelation sequence to polynomial of prediction filter -// -// Calling Sequence -// a = ac2poly(r) -// [a,e] = ac2poly(r) -// -// Parameters -// r: Autocorrelation sequence to be represented with an FIR linear prediction filter -// a: Output polynomial representing the linear prediction filter e/(a(1) + a(2)z + a(3)z^2 .. a(N)z^N-1) -// e: Output scaling for the lienar prediction filter -// -// Description -// Function ac2poly() finds the best fit polynomial for FIR linear prediction filter a, corresponding to the autocorrelation sequence r. a is the same length as r, and is normalized with the first element. So a(1) = 1. -// Author: -// Parthe Pandit -// -// Bibliography -// Kay, Steven M. Modern Spectral Estimation. Englewood Cliffs, NJ: Prentice-Hall, 1988. - -//errcheck -if (type(r) > 1) then - error('Input autocorrelation sequence needs to be of type double'); -end - -[a,e] = levinson(r); -varargout = list(e); - -endfunction |