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Time Varying OLA Modifications

In the preceding sections, we assumed that the spectral modification $ H$ did not vary over time. We will now examine the implications of time-varying spectral modifications. The derivation below follows [10], except that we'll keep our previous notation:

\begin{eqnarray*}
X_m(\omega_k) &=& \hbox{sampled DTFT (FFT) of $m$th input fram...
...ame}\\
N &\ge& \hbox{ $M+L-1$\ to avoid time aliasing in $y_m$}
\end{eqnarray*}

Using $ H_m$ in our OLA formulation with a hop size $ R=1$ results in

\begin{eqnarray*}
y(n) &=& \sum_{m=-\infty}^\infty y_m(n) \\
&=& \sum_{m=-\inf...
...infty}^\infty x(l)
\sum_{m=-\infty}^\infty w(l-m) h_m(n-l) \\
\end{eqnarray*}

Define $ r \mathrel{\stackrel{\Delta}{=}}n-l \;\Rightarrow\; l = n-r$ to get

$\displaystyle y(n)=\sum_{r=-\infty}^\infty x(n-r) \sum_{m=-\infty}^\infty h_m(r) w(n-r-m).
$

Let's examine the term $ \displaystyle\sum_{m=-\infty}^\infty h_m(r) w(
n-r-m )$ in more detail: Using this, we get

\begin{eqnarray*}
y(n) &=& \sum_{r=-\infty}^\infty x(n-r) {\hat h}_{n-r}(r) \\
...
...+ x(n+1) {\hat h}_{n+1}(-1) + x(n+2) {\hat h}_{n+2}(-2) + \cdots
\end{eqnarray*}

This is a superposition sum for an arbitrary linear, time-varying filter $ {\hat h}_{n-r}(r) = [h_{(\cdot)}(r) \ast w](n-r)$ .



Subsections
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[How to cite this work] [Order a printed hardcopy]

``Spectral Audio Signal Processing'', by Julius O. Smith III, (August 2008 Draft).
Copyright © 2008-08-13 by Julius O. Smith III
Center for Computer Research in Music and Acoustics (CCRMA),   Stanford University
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