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decompose f(t) into its frequency components using the


wavelet filterbank (wavelet transform) as Eq. ();

for k:=1 to K do

  $\theta_{1k}(t)=0$
and $\theta_{k}(t)=\theta_{2k}(t)$;


determine Sk(t) and $\phi_k(t)$
from Lemma 1;


determine onset $T_{k,{\rm{on}}}$
and offset $T_{k,{\rm{off}}}$;


the segregated duration is $T_{k,{\rm{on}}}\leq t \leq T_{k,{\rm{off}}}$;

  if Physical constraint 4 or 5 is satisfied ${\bf{then}}$


estimate Ck(t) using the Kalman filter;


determine the interpolated duration;


let I be the number of the interpolated samples;

  for i=1 to I do 

determine the candidates for Ck(t), which 

interpolated by the spline function within 
  $\hat{C}_k(t_i)-P_k(t_i) \leq C_k(t_i) \leq \hat{C}_k(t_i)+P_k(t_i)$;


determine $\hat\theta_k(t)$
from Eq. ();


determine $\hat{A}_k(t)$
from Eq. ();


determine $\hat{\hat{A}}_k(t)$
from Eq. ();


determine ${\rm{Corr}}(\hat{A}_k(t),\hat{\hat{A}}_k(t))$
from Eq. ();

  end


determine Ck(t) when ${\rm{Corr}}(\hat{A}_k(t),\hat{\hat{A}}_k(t))$


becomes a maximum within the estimated 
  -error;


determine $\theta_k(t)$
from Eq. ();

  else


set Ak(t)=0, Bk(t)=Sk(t) and $\theta_k(t)=\phi_k(t)$;

  end


determine Ak(t) and Bk(t) from Eqs. () and (); 

determine each frequency components of f1(t) 

and f2(t) from Eqs. () and ();

end


reconstruct $\hat{f}_1(t)$
and $\hat{f}_2(t)$
using the wavelet filterbank 
  (inverse wavelet transform) from Eqs. () and ();

 

99

decompose f(t) into its frequency components using the


wavelet filterbank (wavelet transform) as Eq. ();

for k:=1 to K do

  $\theta_{1k}(t)=0$
and $\theta_{k}(t)=\theta_{2k}(t)$;


determine Sk(t) and $\phi_k(t)$
from Lemma 1;


determine onset $T_{k,{\rm{on}}}$
and offset $T_{k,{\rm{off}}}$;


the segregated duration is $T_{k,{\rm{on}}}\leq t \leq T_{k,{\rm{off}}}$;

  if Physical constraint 4 or 5 is satisfied ${\bf{then}}$


estimate Ck(t) using the Kalman filter;


determine the interpolated duration;


let I be the number of the interpolated samples;

  for i=1 to I do 

determine the candidates for Ck(t), which 

interpolated by the spline function within 
  $\hat{C}_k(t_i)-P_k(t_i) \leq C_k(t_i) \leq \hat{C}_k(t_i)+P_k(t_i)$;


determine $\hat\theta_k(t)$
from Eq. ();


determine $\hat{A}_k(t)$
from Eq. ();


determine $\hat{\hat{A}}_k(t)$
from Eq. ();


determine ${\rm{Corr}}(\hat{A}_k(t),\hat{\hat{A}}_k(t))$
from Eq. ();

  end


determine Ck(t) when ${\rm{Corr}}(\hat{A}_k(t),\hat{\hat{A}}_k(t))$


becomes a maximum within the estimated 
  -error;


determine $\theta_k(t)$
from Eq. ();

  else


set Ak(t)=0, Bk(t)=Sk(t) and $\theta_k(t)=\phi_k(t)$;

  end


determine Ak(t) and Bk(t) from Eqs. () and (); 

determine each frequency components of f1(t) 

and f2(t) from Eqs. () and ();

end


reconstruct $\hat{f}_1(t)$
and $\hat{f}_2(t)$
using the wavelet filterbank 
  (inverse wavelet transform) from Eqs. () and ();