[SPTK-CVS] SPTK/doc/ref_e lmadf.tex,1.25,1.26

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Update of /cvsroot/sp-tk/SPTK/doc/ref_e
In directory sfp-cvs-1.v30.ch3.sourceforge.com:/tmp/cvs-serv8343

Modified Files:
	lmadf.tex 
Log Message:
add the cascaded filter option and the figures of LMA filter

Index: lmadf.tex
===================================================================
RCS file: /cvsroot/sp-tk/SPTK/doc/ref_e/lmadf.tex,v
retrieving revision 1.25
retrieving revision 1.26
diff -C2 -d -r1.25 -r1.26
*** lmadf.tex	18 Dec 2015 07:53:25 -0000	1.25
--- lmadf.tex	2 Nov 2016 07:13:40 -0000	1.26
***************
*** 42,45 ****
--- 42,46 ----
  % POSSIBILITY OF SUCH DAMAGE.                                       %
  % ----------------------------------------------------------------- %
+ 
  \hypertarget{lmadf}{}
  \name[ref:acep-IEEESP,ref:LMA-IECE]{lmadf}%
***************
*** 47,58 ****
  
  \begin{synopsis}
! \item [lmadf] [ --m $M$ ] [ --p $P$ ] [ --i $I$ ] [ --P $Pa$ ] [ --v ] [ --t ] [ --k ] 
!       {\em cfile} [ {\em infile} ]
  \end{synopsis}
  
  \begin{qsection}{DESCRIPTION}
  {\em lmadf} derives a Log Magnitude Approximation filter 
! from the cepstral coefficients $c(0),c(1),\ldots,c(M)$ in {\em cfile} 
! and uses it to filter an excitation sequence 
  from {\em infile} (or standard input) in order to synthesize speech data, 
  sending the result to standard output.
--- 48,60 ----
  
  \begin{synopsis}
! \item [lmadf] [ --m $M$ ] [ --p $P$ ] [ --i $I$ ] [ --P $Pa$ ] [ --k ]
!   [ --v ] [ --t ] [ --B $B1,B2,...$ ] 
! \item [\ ~~~~]{\em cfile} [ {\em infile} ]
  \end{synopsis}
  
  \begin{qsection}{DESCRIPTION}
  {\em lmadf} derives a Log Magnitude Approximation filter 
! from the cepstral coefficients \\
! $c(0),c(1),\ldots,c(M)$ in {\em cfile} and uses it to filter an excitation sequence 
  from {\em infile} (or standard input) in order to synthesize speech data, 
  sending the result to standard output.
***************
*** 66,79 ****
  H(z) = \exp \sum_{m=0}^{M} c(m) z^{-m}
  \end{displaymath}
! If we remove the gain $K = \exp c(0)$ from the transfer function $H(z)$, then we obtain the following transfer function
  \begin{displaymath}
  D(z) = \exp \sum_{m=1}^{M} c(m) z^{-m},
  \end{displaymath}
! which can be realized using the basic FIR filter
  \begin{displaymath}
  F(z) = \sum_{m=1}^{M} c(m) z^{-m}
  \end{displaymath}
! as shown in Figure \ref{fig:lmadflt_LMA}(a).
! Also, as it can be seen in Figure \ref{fig:lmadflt_LMA}(b),
  the basic filter $F(z)$ can be decomposed as follows
  \begin{displaymath}
--- 68,88 ----
  H(z) = \exp \sum_{m=0}^{M} c(m) z^{-m}
  \end{displaymath}
! If we remove the gain $K = \exp c(0)$ from the transfer function
! $H(z)$, then we obtain the following transfer function.
  \begin{displaymath}
  D(z) = \exp \sum_{m=1}^{M} c(m) z^{-m},
  \end{displaymath}
! The filter $D(z)$ can be constructed as shown in Figure
! \ref{fig:lmadflt_LMA}(a), where the basic
! FIR filter (Figure \ref{fig:lmadflt_LMA}(b)) is
! the following filter.
  \begin{displaymath}
  F(z) = \sum_{m=1}^{M} c(m) z^{-m}
  \end{displaymath}
! If the -t option is assigned, the basic FIR filter (Figure
! \ref{fig:lmadflt_LMA}(b)) are transposed. The transposed FIR filter
! can be constructed as shown in Figure \ref{fig:lmadflt_LMA}(c).
! 
! It can be seen in Figure \ref{fig:lmadflt_LMA}(d),
  the basic filter $F(z)$ can be decomposed as follows
  \begin{displaymath}
***************
*** 87,95 ****
  By doing this decomposition, the accuracy of the approximation
  is improved.
! Also, the values of the coefficients $A_{4,l}$ are given
! in table \ref{tbl:lmadflt_pade}
  \par
  \setcounter{figure}{0}
! \begin{figure}[ht]
  \setlength{\unitlength}{0.9mm}
  \begin{center}
--- 96,108 ----
  By doing this decomposition, the accuracy of the approximation
  is improved.
! 
! The values of the coefficients $A_{4,l}$ are given
! in table \ref{tbl:lmadflt_pade}.
! 
! \newpage
! 
  \par
  \setcounter{figure}{0}
! \begin{figure}[h]
  \setlength{\unitlength}{0.9mm}
  \begin{center}
***************
*** 157,161 ****
  \end{center}
  
! \vspace{2mm}
  \setlength{\unitlength}{0.9mm}
  \begin{center}
--- 170,194 ----
  \end{center}
  
! \vspace{1mm}
! \setlength{\unitlength}{0.9mm}
! \begin{center}
!   \leavevmode
!   \includegraphics{fig/FIRflt.eps}
! \end{center}
! \begin{center}
! (b)
! \end{center}
! 
! \vspace{1mm}
! \setlength{\unitlength}{0.9mm}
! \begin{center}
!   \leavevmode
!   \includegraphics{fig/transflt.eps}
! \end{center}
! \begin{center}
! (c)
! \end{center}
! 
! \vspace{1mm}
  \setlength{\unitlength}{0.9mm}
  \begin{center}
***************
*** 167,185 ****
    \put(47,8){\vector(1,0){10}}
    \put(89,8){\vector(1,0){15}}
!   \put(2,10){\makebox(0,0)[lb]{$x(n)$}}
!   \put(93,10){\makebox(0,0)[lb]{$y(n)$}}
!   \put(0,17){\makebox(0,0)[lb]{Input}}
!   \put(91,17){\makebox(0,0)[lb]{Output}}
  \end{picture}
  \end{center}
  \begin{center}
! (b)
  \end{center}
  \caption{\protect\parbox[t]{8cm}{
  	(a)~~$R_L(F(z))\simeq D(z)$~~~$L=4$ \protect\\
! 	(b)~~2 level cascade realization\protect\\
          \hspace*{5mm} $R_L(F_1(z))\cdot R_L(F_2(z))\simeq D(z)$
  }}
  \label{fig:lmadflt_LMA}
  \end{figure}
  
--- 200,218 ----
    \put(47,8){\vector(1,0){10}}
    \put(89,8){\vector(1,0){15}}
!   \put(0,10){\makebox(0,0)[lb]{Input}}
!   \put(91,10){\makebox(0,0)[lb]{Output}}
  \end{picture}
  \end{center}
  \begin{center}
! (d)
  \end{center}
+ 
  \caption{\protect\parbox[t]{8cm}{
  	(a)~~$R_L(F(z))\simeq D(z)$~~~$L=4$ \protect\\
! 	(d)~~2 level cascade realization\protect\\
          \hspace*{5mm} $R_L(F_1(z))\cdot R_L(F_2(z))\simeq D(z)$
  }}
  \label{fig:lmadflt_LMA}
+ 
  \end{figure}
  
***************
*** 203,208 ****
--- 236,243 ----
  \label{tbl:lmadflt_pade}
  \end{table}
+ 
  \end{qsection}
  
+ 
  \begin{options}
  	\argm{m}{M}{order of cepstrum}{25}
***************
*** 213,217 ****
  	\argm{k}{}{filtering without gain}{FALSE}
  	\argm{v}{}{inverse filter}{FALSE}
! 	\argm{v}{}{transpose filter}{FALSE}
  \end{options}
  
--- 248,255 ----
  	\argm{k}{}{filtering without gain}{FALSE}
  	\argm{v}{}{inverse filter}{FALSE}
! 	\argm{t}{}{transpose filter}{FALSE}
!         \desc[1ex]{(level 2)}
!         \argm{B}{B1~B2~$\ldots$~Bn}{block size of cascaded filter,\\
!         	                     where $(|B1|+\ldots+|Bn|)=m$}{FALSE}
  \end{options}
  
***************
*** 223,232 ****
  {\em data.syn}.
  \begin{quote}
!  \verb!excite < data.pitch | lmadf data.cep > data.syn!
  \end{quote} 
  \end{qsection}
  
  \begin{qsection}{NOTICE}
! $Pa$ = 4 or 5.
  \end{qsection}
  
--- 261,283 ----
  {\em data.syn}.
  \begin{quote}
!  \verb!excite < data.pitch | lmadf -m 15 data.cep > data.syn!
  \end{quote} 
+ If one wants to divide the filter into several blocks, the -B option
+ can be used to specify the block size of each filter.
+ For example, when dividing 15-dimentional filter into 3 sub-parts(4,
+ 5, and 6), the corresponding command line is below.
+ \begin{quote}
+  \verb!excite < data.pitch | lmadf -m 15 -B 4 5 6 data.cep > data.syn!
+ \end{quote}
+ If the block size value assigned by the -B option is negative, the
+ block is not used.
+ \begin{quote}
+  \verb!excite < data.pitch | lmadf -m 15 -B 4 -5 6 data.cep > data.syn!
+ \end{quote}
+ 
  \end{qsection}
  
  \begin{qsection}{NOTICE}
! $Pa$ = 4 or 5.\\
  \end{qsection}