The premise/purpose of the Fourier transform is simple: Any recorded sound can be broken up into discrete sinusoidal components. For instance, a square wave, no matter how perfectly square, can be decomposed into odd-order harmonic sine waves, despite the original square wave looking anything but sinusoidal.
It is not magical however, and the results will vary based on the portion of the recording analyzed.
FFT is also not a substitute for all digital signal analysis. You don’t need FFT to tell you what a visual inspection of an impulse response will, such as looking for reflections and time aligning speakers. Nor do you need FFT to create an algorithm to automatically set speaker delays.
https://en.wikipedia.org/wiki/Fourier_transform
Where the Fast Fourier Transform really transformed acoustics was in the nearly infinite resolution. We went from band limited (octave, 1/3 octave, etc) measurements to resolution bounded only by the sample length on the low end and the Nyquist frequency on the top. Outstanding.
https://en.wikipedia.org/wiki/Nyquist_frequency
And of course, let’s not forget the waterfall plots.
Best,
E
It is not magical however, and the results will vary based on the portion of the recording analyzed.
FFT is also not a substitute for all digital signal analysis. You don’t need FFT to tell you what a visual inspection of an impulse response will, such as looking for reflections and time aligning speakers. Nor do you need FFT to create an algorithm to automatically set speaker delays.
https://en.wikipedia.org/wiki/Fourier_transform
Where the Fast Fourier Transform really transformed acoustics was in the nearly infinite resolution. We went from band limited (octave, 1/3 octave, etc) measurements to resolution bounded only by the sample length on the low end and the Nyquist frequency on the top. Outstanding.
https://en.wikipedia.org/wiki/Nyquist_frequency
And of course, let’s not forget the waterfall plots.
Best,
E