1mlib_SignalLPC2Cepstral_S16m_eAddipa(L3iMbLILBi)brarmyliFbu_nScitginoanlsLPC2Cepstral_S16_Adp(3MLIB)
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NAME

6       mlib_SignalLPC2Cepstral_S16_Adp  -  convert  linear  prediction coeffi‐
7       cients to 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_Adp(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_Adp() function converts linear  predic‐
21       tion  coefficients  to cepstral coefficients. The scaling factor of the
22       output data, cscale, will be calculated based on the actual data.
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25       The cepstral coefficients are the coefficients of the Fourier transform
26       representation of the log magnitude spectrum.
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29       The  LPC  cepstral coefficients can be derived recursively from the LPC
30       coefficients as following.
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32         c(0) = log(G)
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34                       m-1  k
35         c(m) = a(m) + SUM --- * c(k) * a(m-k), 1 ≤ m ≤ M
36                       k=1  m
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38                m-1  k
39         c(m) = SUM --- * c(k) * a(m-k), m > M
40                k=1  m
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44       See Fundamentals of Speech Recognition by Lawrence Rabiner  and  Biing-
45       Hwang Juang, Prentice Hall, 1993.
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PARAMETERS

48       The function takes the following arguments:
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50       cepst     The cepstral coefficients.
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53       cscale    The  scaling  factor  of  the  cepstral  coefficients,  where
54                 actual_data = output_data * 2**(-scaling_factor).
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57       lpc       The linear prediction coefficients.
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60       lscale    The scaling factor of  the  linear  prediction  coefficients,
61                 where actual_data = input_data * 2**(-scaling_factor).
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64       gain      The gain of the LPC model.
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67       gscale    The  scaling  factor  of  the  gain  of  the LPC model, where
68                 actual_data = input_data * 2**(-scaling_factor).
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71       length    The length of the cepstral coefficients.
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74       order     The order of the linear prediction filter.
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RETURN VALUES

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

82       See attributes(5) for descriptions of the following attributes:
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87       ┌─────────────────────────────┬─────────────────────────────┐
88       │      ATTRIBUTE TYPE         │      ATTRIBUTE VALUE        │
89       ├─────────────────────────────┼─────────────────────────────┤
90       │Interface Stability          │Committed                    │
91       ├─────────────────────────────┼─────────────────────────────┤
92       │MT-Level                     │MT-Safe                      │
93       └─────────────────────────────┴─────────────────────────────┘
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SEE ALSO

96       mlib_SignalLPC2Cepstral_S16(3MLIB),                 mlib_SignalLPC2Cep‐
97       stral_S16_Adp(3MLIB), mlib_SignalLPC2Cepstral_F32(3MLIB), attributes(5)
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101SunOS 5.11                        2 Mar 2m0l0i7b_SignalLPC2Cepstral_S16_Adp(3MLIB)