Points to Note

  • We saw that in OLA with time varying modifications and $ R=1$ (a ``sliding'' DFT), the window served as a lowpass filter on each individual tap of the FIR filter being implemented.

  • In the more typical case in which $ R$ is the window length $ M$ divided by a small integer like $ 2$ -$ 10$ , we may think of the window as specifying a type of cross-fade from the LTI filter for one frame to the LTI filter for the next frame.

  • Using a Bartlett (triangular) window with $ 50$ % overlap, ($ R=2$ ), the sequence of FIR filters used is obtained simply by linearly interpolating the LTI filter for one frame to the LTI filter for the next.

  • In FBS, there is no limitation on how fast the filter $ h_m$ may vary with time, but its length is limited to that of the window $ w$ .

  • In OLA, there is no limit on length (just add more zero-padding), but the filter taps are band-limited to the spectral width of the window.

  • FBS filters are time-limited by $ w$ , while OLA filters are band-limited by $ w$ (another dual relation).

  • Recall for comparison that each frame in the OLA method is filtered according to

    $\displaystyle Y_m = X_m \cdot H_m = [X*W_m] \cdot H_m \;\longleftrightarrow\; \underbrace{[x \cdot w_m]}_{x_m} * h_m$ (10.34)

    where $ w_m$ denotes $ \hbox{\sc Shift}_{mR}(w)$ .
  • Time-varying FBS filters are instantly in ``steady state''
  • FBS filters must be changed very slowly to avoid clicks and pops (discontinuity distortion is likely when the filter changes)
For more details, see [9].

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Linear Prediction is Peak Sensitive
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Kaiser Overlap-Add Example