Could you please try this and let me know if this helps.
Above will print the matching line also, if you want to leave that matching line then following may help you in same.
Also considering that there are 98 line present after the match into the Input_file. Let me know on same.
Thanks,
R. Singh
Last edited by RavinderSingh13; 02-03-2016 at 01:46 AM..
This User Gave Thanks to RavinderSingh13 For This Post:
1 . Thanks everyone who read the post first.
2 . I have a log file which size is 143M , I can not use vi open it .I can not use xedit open it too.
How to view it ?
If I want to view 200-300 ,how can I implement it
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I'm new to shell scripting.
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Discussion started by: omega3
9 Replies
LEARN ABOUT SUNOS
mlib_signallpcautocorrelgetenergy_s16
mlib_SignalLPCAutoCorrelGetEnergy_S16(3MLIB) mediaLib Library Functions mlib_SignalLPCAutoCorrelGetEnergy_S16(3MLIB)NAME
mlib_SignalLPCAutoCorrelGetEnergy_S16, mlib_SignalLPCAutoCorrelGetEnergy_S16_Adp - return the energy of the input signal
SYNOPSIS
cc [ flag... ] file... -lmlib [ library... ]
#include <mlib.h>
mlib_status mlib_SignalLPCAutoCorrelGetEnergy_S16(mlib_s16 *engery, mlib_s32 escale, void *state);
mlib_status mlib_SignalLPCAutoCorrelGetEnergy_S16_Adp(mlib_s16 *engery, mlib_s32 *escale, void *state);
DESCRIPTION
Each of the functions returns the energy of the input signal.
In linear predictive coding (LPC) model, each speech sample is represented as a linear combination of the past M samples.
M
s(n) = SUM a(i) * s(n-i) + G * u(n)
i=1
where s(*) is the speech signal, u(*) is the excitation signal, and G is the gain constants, M is the order of the linear prediction fil-
ter. Given s(*), the goal is to find a set of coefficient a(*) that minimizes the prediction error e(*).
M
e(n) = s(n) - SUM a(i) * s(n-i)
i=1
In autocorrelation method, the coefficients can be obtained by solving following set of linear equations.
M
SUM a(i) * r(|i-k|) = r(k), k=1,...,M
i=1
where
N-k-1
r(k) = SUM s(j) * s(j+k)
j=0
are the autocorrelation coefficients of s(*), N is the length of the input speech vector. r(0) is the energy of the speech signal.
Note that the autocorrelation matrix R is a Toeplitz matrix (symmetric with all diagonal elements equal), and the equations can be solved
efficiently with Levinson-Durbin algorithm.
See Fundamentals of Speech Recognition by Lawrence Rabiner and Biing-Hwang Juang, Prentice Hall, 1993.
Note for functions with adaptive scaling (with _Adp postfix), the scaling factor of the output data will be calculated based on the actual
data; for functions with non-adaptive scaling (without _Adp postfix), the user supplied scaling factor will be used and the output will be
saturated if necessary.
PARAMETERS
Each function takes the following arguments:
energy The energy of the input signal.
escale The scaling factor of the energy, where actual_data = output_data * 2**(-scaling_factor).
state Pointer to the internal state structure.
RETURN VALUES
Each function returns MLIB_SUCCESS if successful. Otherwise it returns MLIB_FAILURE.
ATTRIBUTES
See attributes(5) for descriptions of the following attributes:
+-----------------------------+-----------------------------+
| ATTRIBUTE TYPE | ATTRIBUTE VALUE |
+-----------------------------+-----------------------------+
|Interface Stability |Evolving |
+-----------------------------+-----------------------------+
|MT-Level |MT-Safe |
+-----------------------------+-----------------------------+
SEE ALSO mlib_SignalLPCAutoCorrelInit_S16(3MLIB), mlib_SignalLPCAutoCorrel_S16(3MLIB), mlib_SignalLPCAutoCorrelGetPARCOR_S16(3MLIB), mlib_SignalLP-
CAutoCorrelFree_S16(3MLIB), attributes(5)SunOS 5.10 10 Nov 2004 mlib_SignalLPCAutoCorrelGetEnergy_S16(3MLIB)