Chapter Outline

1. ``Introduction to the DFT'' points out the mathematical elements which will be discussed in this book, all motivated by the DFT.

2. ``Introduction to Complex Numbers'' is about factoring polynomials, the quadratic formula, the complex plane, and Euler's formula.

3. ``Proof of Euler's Identity'' derives Euler's identity in detail. This is an important tool for working with complex numbers, and one of the critical elements of the DFT definition we need to understand.

4. ``Sinusoids and Exponentials'' is devoted to two of the most elementary signals in signal processing--sinusoids and exponentials. Also covered are complex sinusoids, audio decay time ($t_{60}$), in-phase and quadrature sinusoidal components, analytic signals, positive and negative frequencies, constructive and destructive interference, invariance of sinusoidal frequency in linear time-invariant systems, circular motion as the vector sum of in-phase and quadrature sinusoidal motions, sampled sinusoids, and generating sampled sinusoids from powers of a unit-modulus complex number.

5. ``Geometric Signal Theory'' provides an introduction to vector spaces, inner products, orthogonality, projection of one signal onto another, norms, and elementary vector space operations. In this setting, the DFT can be regarded as a change of coordinates from one basis set (shifted impulses) to another (sinusoids at different frequencies).

6. ``The DFT Derived'' derives the DFT as a projection of a length $N$ signal $x(\cdot)$ onto the set of $N$ sampled complex sinusoids generated by the $N$th roots of unity.

7. ``Fourier Theorems for the DFT'' derives basic Fourier symmetry relations, the shift theorem, convolution theorem, correlation theorem, power theorem, and theorems pertaining to interpolation and downsampling.

8. ``Example Applications of the DFT'' illustrates practical FFT analysis in Matlab $^{\hbox{\scriptsize\textcircled{\tiny R}}}$ and Octave (an open-source matlab) through a series of examples. The various Fourier theorems of the preceding chapter provide a ``thinking vocabulary'' for understanding these applications.

Elementary and supporting information is provided in a series of appendices. Topics include introductions to sampling theory, Taylor series expansions, logarithms, decibels, digital audio number systems, matrices, round-off noise, Fourier series, and continuous-time Fourier theorems, such as the similarity and differentiation theorems. As a segue to computer-based approaches, a well used Fast Fourier Transform (FFT) algorithm is derived. Finally, various software examples in the Matlab (or Octave) programming language are presented.

This book is first in a series of course readers for my signal processing courses at CCRMA:

1. Mathematics of the Discrete Fourier Transform (DFT):
   http://ccrma.stanford.edu/~jos/mdft/
2. Introduction to Digital Filters:
   http://ccrma.stanford.edu/~jos/filters/
3. Physical Audio Signal Processing
   (the ``physical modeling book''):
   http://ccrma.stanford.edu/~jos/pasp/
4. Spectral Audio Signal Processing
   (the ``spectral modeling book''):
   http://ccrma.stanford.edu/~jos/sasp/
5. Audio Digital Filter Design
   (an update of Chapter 1 of my PhD/EE thesis [64]):
   http://ccrma.stanford.edu/~jos/adfd/