diff options
Diffstat (limited to 'gr-vocoder/lib/codec2/lpc.c')
| -rw-r--r-- | gr-vocoder/lib/codec2/lpc.c | 136 |
1 files changed, 68 insertions, 68 deletions
diff --git a/gr-vocoder/lib/codec2/lpc.c b/gr-vocoder/lib/codec2/lpc.c index ba8011377..1784f75ca 100644 --- a/gr-vocoder/lib/codec2/lpc.c +++ b/gr-vocoder/lib/codec2/lpc.c @@ -1,11 +1,11 @@ /*---------------------------------------------------------------------------*\ - - FILE........: lpc.c - AUTHOR......: David Rowe - DATE CREATED: 30/9/90 - - Linear Prediction functions written in C. - + + FILE........: lpc.c + AUTHOR......: David Rowe + DATE CREATED: 30/9/90 + + Linear Prediction functions written in C. + \*---------------------------------------------------------------------------*/ /* @@ -34,11 +34,11 @@ #include "lpc.h" /*---------------------------------------------------------------------------*\ - - hanning_window() - - Hanning windows a frame of speech samples. - + + hanning_window() + + Hanning windows a frame of speech samples. + \*---------------------------------------------------------------------------*/ void hanning_window( @@ -54,12 +54,12 @@ void hanning_window( } /*---------------------------------------------------------------------------*\ - - autocorrelate() - - Finds the first P autocorrelation values of an array of windowed speech - samples Sn[]. - + + autocorrelate() + + Finds the first P autocorrelation values of an array of windowed speech + samples Sn[]. + \*---------------------------------------------------------------------------*/ void autocorrelate( @@ -79,12 +79,12 @@ void autocorrelate( } /*---------------------------------------------------------------------------*\ - - autocorrelate_freq() - + + autocorrelate_freq() + Finds the first P autocorrelation values from an array of frequency domain - power samples. - + power samples. + \*---------------------------------------------------------------------------*/ void autocorrelate_freq( @@ -106,18 +106,18 @@ void autocorrelate_freq( } /*---------------------------------------------------------------------------*\ - - levinson_durbin() - - Given P+1 autocorrelation coefficients, finds P Linear Prediction Coeff. + + levinson_durbin() + + Given P+1 autocorrelation coefficients, finds P Linear Prediction Coeff. (LPCs) where P is the order of the LPC all-pole model. The Levinson-Durbin - algorithm is used, and is described in: - - J. Makhoul - "Linear prediction, a tutorial review" - Proceedings of the IEEE - Vol-63, No. 4, April 1975 - + algorithm is used, and is described in: + + J. Makhoul + "Linear prediction, a tutorial review" + Proceedings of the IEEE + Vol-63, No. 4, April 1975 + \*---------------------------------------------------------------------------*/ void levinson_durbin( @@ -152,18 +152,18 @@ void levinson_durbin( for(i=1; i<=order; i++) lpcs[i] = a[order][i]; - lpcs[0] = 1.0; + lpcs[0] = 1.0; } /*---------------------------------------------------------------------------*\ - - inverse_filter() - + + inverse_filter() + Inverse Filter, A(z). Produces an array of residual samples from an array - of input samples and linear prediction coefficients. - + of input samples and linear prediction coefficients. + The filter memory is stored in the first order samples of the input array. - + \*---------------------------------------------------------------------------*/ void inverse_filter( @@ -180,26 +180,26 @@ void inverse_filter( res[i] = 0.0; for(j=0; j<=order; j++) res[i] += Sn[i-j]*a[j]; - } + } } /*---------------------------------------------------------------------------*\ - - synthesis_filter() - - C version of the Speech Synthesis Filter, 1/A(z). Given an array of + + synthesis_filter() + + C version of the Speech Synthesis Filter, 1/A(z). Given an array of residual or excitation samples, and the the LP filter coefficients, this function will produce an array of speech samples. This filter structure is - IIR. - - The synthesis filter has memory as well, this is treated in the same way - as the memory for the inverse filter (see inverse_filter() notes above). - The difference is that the memory for the synthesis filter is stored in + IIR. + + The synthesis filter has memory as well, this is treated in the same way + as the memory for the inverse filter (see inverse_filter() notes above). + The difference is that the memory for the synthesis filter is stored in the output array, wheras the memory of the inverse filter is stored in the - input array. - - Note: the calling function must update the filter memory. - + input array. + + Note: the calling function must update the filter memory. + \*---------------------------------------------------------------------------*/ void synthesis_filter( @@ -222,12 +222,12 @@ void synthesis_filter( } /*---------------------------------------------------------------------------*\ - - find_aks() - - This function takes a frame of samples, and determines the linear - prediction coefficients for that frame of samples. - + + find_aks() + + This function takes a frame of samples, and determines the linear + prediction coefficients for that frame of samples. + \*---------------------------------------------------------------------------*/ void find_aks( @@ -257,11 +257,11 @@ void find_aks( } /*---------------------------------------------------------------------------*\ - - weight() - - Weights a vector of LPCs. - + + weight() + + Weights a vector of LPCs. + \*---------------------------------------------------------------------------*/ void weight( @@ -272,8 +272,8 @@ void weight( ) { int i; - + for(i=1; i<=order; i++) akw[i] = ak[i]*pow(gamma,(float)i); } - + |