Another multi-dimensional abstraction for music that's at a bit higher level is a musical lattice. The number of dimensions is limited by the largest prime you accept in the ratios you use. (Typical western music approximated by 12-tone equal temperament uses primes of 2, 3, and 5. Powers of 2 are often ignored because notes an octave apart are perceived to be sort of equivalent for harmonic purposes.)
Spectrogram is 2D (plot of amplitude given time and frequency).
Its interesting to think about it for a spectrogram because "similarity" is different in each dimension (freq vs. time).
Frequency is also perceived logarithmically, so you would probably want to convert to e.g. Mel scale before applying this algorithm (a 2000-2100Hz change is much subtler than a 200-300Hz change).
It should be, at least on audio level it would work. It would make a nice VST :)
Something similar does exist: http://labs.echonest.com/Uploader/index.html
It detects places in songs where the sound is similar, and can jump between these places in the track to make a seamless unending song.
I doubt it, because music is 1-dimensional and for 1-dimensional arrays WFC is just a Markov chain.
There are three-dimensional views of music, e.g. time-frequency-amplitude. See https://en.wikipedia.org/wiki/Spectrogram
Another multi-dimensional abstraction for music that's at a bit higher level is a musical lattice. The number of dimensions is limited by the largest prime you accept in the ratios you use. (Typical western music approximated by 12-tone equal temperament uses primes of 2, 3, and 5. Powers of 2 are often ignored because notes an octave apart are perceived to be sort of equivalent for harmonic purposes.)
https://en.wikipedia.org/wiki/Lattice_(music)
Spectrogram is 2D (plot of amplitude given time and frequency).
Its interesting to think about it for a spectrogram because "similarity" is different in each dimension (freq vs. time). Frequency is also perceived logarithmically, so you would probably want to convert to e.g. Mel scale before applying this algorithm (a 2000-2100Hz change is much subtler than a 200-300Hz change).
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How is music 1-dimensional?
A microphone records amplitude of sound waves over time, i.e. the air pressure -- that's the dimension.
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Perhaps possible to slice the music wave data into frames, perform FFT to get a spectrogram and use that as tiles?