Chapter 6. Digital Filters


In the previous chapters we developed a number of tools for working with signals. In order to keep the discussion as tight as possible, these tools were generally presented in a context where they could be understood independently. Convolution, for example, was presented as a generalization of the moving average filter. In a similar manner, the DFT was shown to be a tool that mapped a function of time (the signal) to a function of frequency (the signal's spectrum). We also pointed out, though we did not demonstrate it, that the DFT was a reversible function: given a signal's spectrum, we could use the DFT to get the signal.

It is now time to start tying these tools together to develop a more sophisticated ...

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