The Fast Fourier Transform is a cornerstone of audio processing providing a representation of a time domain signal in the frequency domain. The ability to manipulate signals in the frequency domain gives us the power to isolate, enhance, and sculpt specific frequency components, redefining the very essence of sound. However, for this to work, an accurate frequency domain representation is essential.

As DSP enthusiasts, we tend to treat the FFT as a magic bullet solution, a library function that is so commonplace, we can implement it with an “include” statement and a few lines of code. But what do we really know about its inner workings, its nuances and most importantly, its limitations? Can we be confident that the frequencies reported by the FFT are truly present in the original signal, or is our ignorance of how it works pulling the wool over our eyes and causing unwanted artefacts in our output?

In this presentation we’re going to lift the hood on the FFT and discover:

1. How the Fourier Transform works
2. Why the FFT is only an approximation (albeit a good one) of the true Fourier Transform
3. What the limitations of the FFT’s approximation are
4. How to mitigate these limitations by windowing your signal before it enters the FFT
5. How to navigate the trade-off between processing speed and frequency resolution in the FFT

This presentation aims to equip you with a practical understanding of the FFT, empowering you to create higher-quality and more precise DSP applications.