From f919f9dcbb54a08e6e26d6c229ce92fb784fa1b2 Mon Sep 17 00:00:00 2001
From: Tom Rondeau <trondeau@vt.edu>
Date: Fri, 13 Apr 2012 18:36:53 -0400
Subject: Removed whitespace and added dtools/bin/remove-whitespace as a tool
to do this in the future.
The sed script was provided by Moritz Fischer.
---
gr-vocoder/lib/codec2/lpc.c | 136 ++++++++++++++++++++++----------------------
1 file changed, 68 insertions(+), 68 deletions(-)
(limited to 'gr-vocoder/lib/codec2/lpc.c')
diff --git a/gr-vocoder/lib/codec2/lpc.c b/gr-vocoder/lib/codec2/lpc.c
index ba80113773..1784f75ca1 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);
}
-
+
--
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