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Bandlimited Interpolation of Time-Limited Signals

The previous result can be extended toward bandlimited interpolation of $ x\in{\bf C}^{N_x}$ which includes all nonzero samples from an arbitrary time-limited signal $ x^\prime\in {\bf C}^\infty$ (i.e., going beyond the interpolation of only periodic bandlimited signals given one or more periods $ x\in{\bf C}^N$ ) by

  1. replacing the rectangular window $ \hbox{\sc Chop}_N()$ with a smoother spectral window $ H(\omega)$ , and
  2. using extra zero-padding in the time domain to convert the cyclic convolution between $ \hbox{\sc Stretch}_L(x)$ and $ h$ into an acyclic convolution between them (recall §7.2.4).
The smoother spectral window $ H$ can be thought of as the frequency response of the FIR7.23 filter $ h$ used as the bandlimited interpolation kernel in the time domain. The number of zeros needed in the zero-padding of $ x$ in the time domain is simply length of $ h$ minus 1, and the number of zeros to be appended to $ h$ is the length of $ \hbox{\sc Stretch}_L(x)$ minus 1. With this much zero-padding, the cyclic convolution of $ x$ and $ h$ implemented using the DFT becomes equivalent to acyclic convolution, as desired for the time-limited signals $ x$ and $ h$ . Thus, if $ N_x$ denotes the nonzero length of $ x$ , then the nonzero length of $ \hbox{\sc Stretch}_L(x)$ is $ L(N_x-1)+1$ , and we require the DFT length to be $ N\geq
L(N_x-1)+N_h$ , where $ N_h$ is the filter length. In operator notation, we can express bandlimited sampling-rate up-conversion by the factor $ L$ for time-limited signals $ x\in{\bf C}^{N_x}$ by

$\displaystyle \zbox {\hbox{\sc Interp}_L(x) \approx \hbox{\sc IDFT}(H\cdot(\hbox{\sc DFT}(\hbox{\sc ZeroPad}_{N}(\hbox{\sc Stretch}_L(x)))).} \protect$ (7.8)

The approximation symbol `$ \approx$ ' approaches equality as the spectral window $ H$ approaches $ \hbox{\sc Chop}_{N_x}([1,\dots,1])$ (the frequency response of the ideal lowpass filter passing only the original spectrum $ X$ ), while at the same time allowing no time aliasing (convolution remains acyclic in the time domain).

Equation (7.8) can provide the basis for a high-quality sampling-rate conversion algorithm. Arbitrarily long signals can be accommodated by breaking them into segments of length $ N_x$ , applying the above algorithm to each block, and summing the up-sampled blocks using overlap-add. That is, the lowpass filter $ h$ ``rings'' into the next block and possibly beyond (or even into both adjacent time blocks when $ h$ is not causal), and this ringing must be summed into all affected adjacent blocks. Finally, the filter $ H$ can ``window away'' more than the top $ L-1$ copies of $ X$ in $ Y$ , thereby preparing the time-domain signal for downsampling, say by $ M\in{\bf Z}$ :

$\displaystyle {\footnotesize
\zbox {\hbox{\sc Interp}_{L/M}(x) \approx \hbox{\sc Downsample}_M(\hbox{\sc IDFT}(H\cdot(\hbox{\sc DFT}(\hbox{\sc ZeroPad}_{N}(\hbox{\sc Stretch}_L(x)))))}
}
$

where now the lowpass filter frequency response $ H$ must be close to zero for all $ \vert\omega_k\vert\geq\pi/\max(L,M)$ . While such a sampling-rate conversion algorithm can be made more efficient by using an FFT in place of the DFT (see Appendix A), it is not necessarily the most efficient algorithm possible. This is because (1) $ M-1$ out of $ M$ output samples from the IDFT need not be computed at all, and (2) $ \hbox{\sc Stretch}_L(x)$ has many zeros in it which do not need explicit handling. For an introduction to time-domain sampling-rate conversion (bandlimited interpolation) algorithms which take advantage of points (1) and (2) in this paragraph, see, e.g., Appendix D and [74].


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``Mathematics of the Discrete Fourier Transform (DFT), with Audio Applications --- Second Edition'', by Julius O. Smith III, W3K Publishing, 2007, ISBN 978-0-9745607-4-8.
Copyright © 2014-04-06 by Julius O. Smith III
Center for Computer Research in Music and Acoustics (CCRMA),   Stanford University
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