In the signal processing literature, it is common to write the DFT and its inverse in the more pure form below, obtained by setting in the previous definition:
where denotes the input signal at time (sample) , and denotes the th spectral sample. This form is the simplest mathematically, while the previous form is easier to interpret physically.
There are two remaining symbols in the DFT we have not yet defined:
The first, , is the basis for complex numbers.1.1 As a result, complex numbers will be the first topic we cover in this book (but only to the extent needed to understand the DFT).
The second, , is a (transcendental) real number defined by the above limit. We will derive and talk about why it comes up in Chapter 3.
Note that not only do we have complex numbers to contend with, but we have them appearing in exponents, as in
With , , and imaginary exponents understood, we can go on to prove Euler's Identity:
Finally, we need to understand what the summation over is doing in the definition of the DFT. We'll learn that it should be seen as the computation of the inner product of the signals and defined above, so that we may write the DFT, using inner-product notation, as
After the foregoing, the inverse DFT can be understood as the sum of projections of onto ; i.e., we'll show
Having completely understood the DFT and its inverse mathematically, we go on to proving various Fourier Theorems, such as the ``shift theorem,'' the ``convolution theorem,'' and ``Parseval's theorem.'' The Fourier theorems provide a basic thinking vocabulary for working with signals in the time and frequency domains. They can be used to answer questions such as
``What happens in the frequency domain if I do [operation x] in the time domain?''Usually a frequency-domain understanding comes closest to a perceptual understanding of audio processing.
Finally, we will study a variety of practical spectrum analysis examples, using primarily the matlab programming language  to analyze and display signals and their spectra.