### Zero Padding Applications

Zero padding in the time domain is used extensively in practice to compute heavily*interpolated*spectra by taking the DFT of the zero-padded signal. Such spectral interpolation is ideal when the original signal is

*time limited*(nonzero only over some finite duration spanned by the orignal samples).

Note that the

*time-limited*assumption directly contradicts our usual assumption of

*periodic extension*. As mentioned in §6.7, the interpolation of a periodic signal's spectrum from its harmonics is always zero; that is, there is no spectral energy, in principle, between the harmonics of a periodic signal, and a periodic signal cannot be time-limited unless it is the zero signal. On the other hand, the interpolation of a time-limited signal's spectrum is nonzero almost everywhere between the original spectral samples. Thus, zero-padding is often used when analyzing data from a non-periodic signal in blocks, and each block, or

*frame*, is treated as a finite-duration signal which can be zero-padded on either side with any number of zeros. In summary, the use of zero-padding corresponds to the

*time-limited assumption*for the data frame, and more zero-padding yields denser interpolation of the frequency samples around the unit circle. Sometimes people will say that zero-padding in the time domain yields higher spectral

*resolution*in the frequency domain. However, signal processing practitioners should not say that, because ``resolution'' in signal processing refers to the ability to ``resolve'' closely spaced features in a spectrum analysis (see Book IV [70] for details). The usual way to increase spectral resolution is to take a longer DFT

*without*zero padding--

*i.e.*, look at more data. In the field of

*graphics*, the term resolution refers to pixel density, so the common terminology confusion is reasonable. However, remember that in signal processing, zero-padding in one domain corresponds to a higher interpolation-density in the other domain--not a higher resolution.

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Ideal Spectral Interpolation

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Causal Zero Padding