This post looks at Fast Fourier Transformation (FFT) and its relationship to my life and spectralism. And yes, it’s more than a bit nerdy.
Over the past couple of weeks, I’ve been listening closely to lots of sounds around me while viewing their FFT data. What I’ve been trying to do is to match my experience of sound with a way of visualizing sound. Even though these charts are easy to read, they often seem abstracted from how things sound.
First off, what is Fast Fourier Transformation? Named after Joseph Fourier (1768–1830), the idea here is that any sound can be broken down into its composite parts in their simple form – sine waves. FFT shows the parts of sounds in ‘frequency bins’. So when I sing a single note – as in the image you see of me singing A3 – it is actually made up of other frequencies (overtones/partials). The ‘fast’ part means (in simple terms) that the analysis is done almost in real time.
I’ve been using two ios apps to do the FFT work. Just a few years ago, you would have needed an expensive dedicated machine (of course these apps are not as good as dedicated hardware, but are quite good). The apps are FFT ($25) and SpectrumView (free).
The images in this post from the apps listed above represent two different ways to visualize sound spectra information. One view shows one moment in time, with the amplitude (volume) on the y-axis and the frequency on the x-axis. This is very helpful for detailed analysis at one point in time. The other graph, often called a spectrogram, includes the element of time. Time in seconds is on the x-axis, frequency is on the y-axis, and amplitude is represented by colour or darkness. Here’s a link to a gallery I’ve made with comments on the relationship between sounds and their images.
Spectralist composers frequently use spectral data for the creation of music. Here is Gerard Grisey’s Partiels (I mentioned it last post) which uses the Fourier analysis of the overtones of a trombone as compositional material.
Spectralist composers are often interested in using this data to go beyond music as discreet notes and explore and manipulate how sound is experienced. Grisey’s composition uses ‘orchestral synthesis’ in that it synthesizes another sound with the sound on an orchestra. FFT data is also used for electronic synthesis of sound. Yes, that includes the sounds of the keytar.