1mlib_SignalLPCAutoCorrelGetmEendeiragLyi_bSm1Ll6ii(bb3r_MaSLriIygBn)FaulnLcPtCiAountsoCorrelGetEnergy_S16(3MLIB)
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NAME

6       mlib_SignalLPCAutoCorrelGetEnergy_S16,   mlib_SignalLPCAutoCorrelGetEn‐
7       ergy_S16_Adp - return the energy of the input signal
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SYNOPSIS

10       cc [ flag... ] file... -lmlib [ library... ]
11       #include <mlib.h>
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13       mlib_status mlib_SignalLPCAutoCorrelGetEnergy_S16(
14            mlib_s16 *engery, mlib_s32 escale, void *state);
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17       mlib_status mlib_SignalLPCAutoCorrelGetEnergy_S16_Adp(
18            mlib_s16 *engery, mlib_s32 *escale, void *state);
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20

DESCRIPTION

22       Each of the functions returns the energy of the input signal.
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25       In linear predictive coding (LPC) model, each speech sample  is  repre‐
26       sented as a linear combination of the past M samples.
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28                      M
29              s(n) = SUM a(i) * s(n-i) + G * u(n)
30                     i=1
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32
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34       where  s(*)  is the speech signal, u(*) is the excitation signal, and G
35       is the gain constants, M is the order of the linear prediction  filter.
36       Given  s(*),  the  goal is to find a set of coefficient a(*) that mini‐
37       mizes the prediction error e(*).
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39                             M
40              e(n) = s(n) - SUM a(i) * s(n-i)
41                            i=1
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45       In autocorrelation method, the coefficients can be obtained by  solving
46       following set of linear equations.
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48               M
49              SUM a(i) * r(|i-k|) = r(k), k=1,...,M
50              i=1
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54       where
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56                    N-k-1
57              r(k) = SUM s(j) * s(j+k)
58                     j=0
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62       are  the  autocorrelation  coefficients of s(*), N is the length of the
63       input speech vector. r(0) is the energy of the speech signal.
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66       Note that the autocorrelation matrix R is a Toeplitz matrix  (symmetric
67       with  all  diagonal  elements  equal),  and the equations can be solved
68       efficiently with Levinson-Durbin algorithm.
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71       See Fundamentals of Speech Recognition by Lawrence Rabiner  and  Biing-
72       Hwang Juang, Prentice Hall, 1993.
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75       Note for functions with adaptive scaling (with _Adp postfix), the scal‐
76       ing factor of the output data will be calculated based  on  the  actual
77       data;  for  functions with non-adaptive scaling (without _Adp postfix),
78       the user supplied scaling factor will be used and the  output  will  be
79       saturated if necessary.
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PARAMETERS

82       Each function takes the following arguments:
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84       energy    The energy of the input signal.
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87       escale    The  scaling  factor  of the energy, where actual_data = out‐
88                 put_data * 2**(-scaling_factor).
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91       state     Pointer to the internal state structure.
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RETURN VALUES

95       Each function returns MLIB_SUCCESS if successful. Otherwise it  returns
96       MLIB_FAILURE.
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ATTRIBUTES

99       See attributes(5) for descriptions of the following attributes:
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104       ┌─────────────────────────────┬─────────────────────────────┐
105       │      ATTRIBUTE TYPE         │      ATTRIBUTE VALUE        │
106       ├─────────────────────────────┼─────────────────────────────┤
107       │Interface Stability          │Committed                    │
108       ├─────────────────────────────┼─────────────────────────────┤
109       │MT-Level                     │MT-Safe                      │
110       └─────────────────────────────┴─────────────────────────────┘
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SEE ALSO

113       mlib_SignalLPCAutoCorrelInit_S16(3MLIB),         mlib_SignalLPCAutoCor‐
114       rel_S16(3MLIB), mlib_SignalLPCAutoCorrelGetPARCOR_S16(3MLIB), mlib_Sig‐
115       nalLPCAutoCorrelFree_S16(3MLIB), attributes(5)
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119SunOS 5.11                        2mlMiabr_S2i0g0n7alLPCAutoCorrelGetEnergy_S16(3MLIB)
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