1mlib_SignalLPC2Cepstral_S16m(e3dMiLaILBi)b Library Fumnlcitbi_oSnisgnalLPC2Cepstral_S16(3MLIB)
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NAME

6       mlib_SignalLPC2Cepstral_S16 - convert linear prediction coefficients to
7       cepstral coefficients
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SYNOPSIS

10       cc [ flag... ] file... -lmlib [ library... ]
11       #include <mlib.h>
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13       mlib_status mlib_SignalLPC2Cepstral_S16(mlib_s16 *cepst,
14            mlib_s32 cscale, const mlib_s16 *lpc, mlib_s32 lscale,
15            mlib_s16 gain, mlib_s32 gscale, mlib_s32 length,
16            mlib_s32 order);
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DESCRIPTION

20       The mlib_SignalLPC2Cepstral_S16() function converts  linear  prediction
21       coefficients  to  cepstral coefficients. The user supplied scaling fac‐
22       tor, cscale, will be used and the output will be  saturated  if  neces‐
23       sary.
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26       The cepstral coefficients are the coefficients of the Fourier transform
27       representation of the log magnitude spectrum.
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30       The LPC cepstral coefficients can be derived recursively from  the  LPC
31       coefficients as following.
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33         c(0) = log(G)
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35                       m-1  k
36         c(m) = a(m) + SUM --- * c(k) * a(m-k), 1 ≤ m ≤ M
37                       k=1  m
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39                m-1  k
40         c(m) = SUM --- * c(k) * a(m-k), m > M
41                k=1  m
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45       See  Fundamentals  of Speech Recognition by Lawrence Rabiner and Biing-
46       Hwang Juang, Prentice Hall, 1993.
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PARAMETERS

49       The function takes the following arguments:
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51       cepst     The cepstral coefficients.
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54       cscale    The  scaling  factor  of  the  cepstral  coefficients,  where
55                 actual_data = output_data * 2**(-scaling_factor).
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58       lpc       The linear prediction coefficients.
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61       lscale    The  scaling  factor  of  the linear prediction coefficients,
62                 where actual_data = input_data * 2**(-scaling_factor).
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65       gain      The gain of the LPC model.
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68       gscale    The scaling factor of  the  gain  of  the  LPC  model,  where
69                 actual_data = input_data * 2**(-scaling_factor).
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72       length    The length of the cepstral coefficients.
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75       order     The order of the linear prediction filter.
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RETURN VALUES

79       The  function  returns MLIB_SUCCESS if successful. Otherwise it returns
80       MLIB_FAILURE.
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ATTRIBUTES

83       See attributes(5) for descriptions of the following attributes:
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88       ┌─────────────────────────────┬─────────────────────────────┐
89       │      ATTRIBUTE TYPE         │      ATTRIBUTE VALUE        │
90       ├─────────────────────────────┼─────────────────────────────┤
91       │Interface Stability          │Committed                    │
92       ├─────────────────────────────┼─────────────────────────────┤
93       │MT-Level                     │MT-Safe                      │
94       └─────────────────────────────┴─────────────────────────────┘
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SEE ALSO

97       mlib_SignalLPC2Cepstral_S16(3MLIB),                 mlib_SignalLPC2Cep‐
98       stral_S16_Adp(3MLIB), mlib_SignalLPC2Cepstral_F32(3MLIB), attributes(5)
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102SunOS 5.11                        2 Mar 2007mlib_SignalLPC2Cepstral_S16(3MLIB)
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