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mlib_SignalDTWKScalar_S16(mediaLib Library Funmlib_SignalDTWKScalar_S16(3MLIB)

NAME
       mlib_SignalDTWKScalar_S16  -  perform  dynamic  time warping for K-best
       paths on scalar data

SYNOPSIS
       cc [ flag... ] file... -lmlib [ library... ]
       #include <mlib.h>

       mlib_status mlib_SignalDTWKScalar_S16(mlib_d64 *dist,
	   const mlib_s16 *dobs, mlib_s32 lobs, mlib_s32 sobs,
	   void *state);

DESCRIPTION
       The mlib_SignalDTWKScalar_S16() function performs dynamic time  warping
       for K-best paths on scalar data.

       Assume the reference data are

	     r(y), y=1,2,...,N

       and the observed data are

	     o(x), x=1,2,...,M

       the dynamic time warping is to find a mapping function (a path)

	     p(i) = {px(i),py(i)}, i=1,2,...,Q

       with the minimum distance.

       In  K-best  paths  case,	 K  paths  with	 the  K	 minimum distances are
       searched.

       The distance of a path is defined as

		     Q
	    dist = SUM d(r(py(i)),o(px(i))) * m(px(i),py(i))
		   i=1

       where d(r,o) is the dissimilarity between data point/vector r and  data
       point/vector  o;	 m(x,y)	 is  the path weighting coefficient associated
       with path point (x,y); N is the length of the reference data; M is  the
       length of the observed data; Q is the length of the path.

       Using L1 norm (sum of absolute differences)

		      L-1
	    d(r,o) = SUM |r(i) - o(i)|
		     i=0

       Using L2 norm (Euclidean distance)

			     L-1
	    d(r,o) = SQRT { SUM (r(i) - o(i))**2 }
			    i=0

       where L is the length of each data vector.

       To scalar data where L=1, the two norms are the same.

	     d(r,o) = |r - o| = SQRT {(r - o)**2 }

       The constraints of dynamic time warping are:

	   1.	  Endpoint constraints

			px(1) = 1
		       1 ≤ py(1) ≤ 1 + delta

		  and

			px(Q) = M
		       N-delta ≤ py(Q) ≤ N

	   2.	  Monotonicity Conditions

			px(i) ≤ px(i+1)
		       py(i) ≤ py(i+1)

	   3.	  Local Continuity Constraints

		  See Table 4.5 on page 211 in Rabiner and Juang's book.

		  Itakura Type:

			py
		       |
		       *----*----*
		       |p4  |p1	 |p0
		       |    |	 |
		       *----*----*
		       |    |p2	 |
		       |    |	 |
		       *----*----*-- px
			     p3

		  Allowable paths are

			p1->p0	  (1,0)
		       p2->p0	 (1,1)
		       p3->p0	 (1,2)

		  Consecutive  (1,0)(1,0) is disallowed. So path p4->p1->p0 is
		  disallowed.

	   4.	  Global Path Constraints

		  Due to local continuity constraints, certain portions of the
		  (px,py) plane are excluded from the region the optimal warp‐
		  ing path can traverse. This forms global path constraints.

	   5.	  Slope Weighting

		  See Equation 4.150-3 on page	216  in	 Rabiner  and  Juang's
		  book.

       A  path in (px,py) plane can be represented in chain code. The value of
       the chain code is defined as following.

	     ============================
	    shift ( x , y ) | chain code
	    ----------------------------
		( 1 , 0 )   |	  0
		( 0 , 1 )   |	  1
		( 1 , 1 )   |	  2
		( 2 , 1 )   |	  3
		( 1 , 2 )   |	  4
		( 3 , 1 )   |	  5
		( 3 , 2 )   |	  6
		( 1 , 3 )   |	  7
		( 2 , 3 )   |	  8
	    ============================

		py
		|
		*  8  7	 *
		|
		*  4  *	 6
		|
		1  2  3	 5
		|
		x--0--*--*-- px

       where x marks the start point of a path segment, the  numbers  are  the
       values of the chain code for the segment that ends at the point.

       In  following example, the observed data with 11 data points are mapped
       into the reference data with 9 data points

		 py
		|
	     9	| * * * * * * * * * *-*
		|		   /
		| * * * * * * * *-* * *
		|	       /
		| * * * * * * * * * * *
		|	     /
		| * * * * *-* * * * * *
		|	 /
		| * * * * * * * * * * *
		|	|
		| * * * * * * * * * * *
		|      /
		| * * * * * * * * * * *
		|    /
		| * * * * * * * * * * *
		|  /
	     1	| * * * * * * * * * * *
		|
		+------------------------ px
		  1		      11

       The chain code that represents the path is

	     (2 2 2 1 2 0 2 2 0 2 0)

       See Fundamentals of Speech Recognition by Lawrence Rabiner  and	Biing-
       Hwang Juang, Prentice Hall, 1993.

PARAMETERS
       The function takes the following arguments:

       dist	The distances of the K-best paths.

       dobs	The observed data array.

       lobs	The length of the observed data array.

       sobs	The   scaling	factor	of  the	 observed  data	 array,	 where
		actual_data = input_data * 2**(-scaling_factor).

       state	Pointer to the internal state structure.

RETURN VALUES
       The 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	     │Committed			   │
       ├─────────────────────────────┼─────────────────────────────┤
       │MT-Level		     │MT-Safe			   │
       └─────────────────────────────┴─────────────────────────────┘

SEE ALSO
       mlib_SignalDTWKScalarInit_S16(3MLIB), mlib_SignalDTWKScalar_S16(3MLIB),
       mlib_SignalDTWKScalarPath_S16(3MLIB),		mlib_SignalDTWKScalar‐
       Free_S16(3MLIB), attributes(5)

SunOS 5.10			  23 May 2007 mlib_SignalDTWKScalar_S16(3MLIB)
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