[PD] OT: faster than Fourier transform

[Ed Kelly]

I read about this in New Scientist. It seemed interesting at first, but I am fairly sure that it is quite specific in its applications. It reminds me of the mpeg 1 layer 3 approach, which is to "psychoacoustically" encode the audio with less data by removing parts of the signal we "don't hear". I think the application (telecoms) is more concerned with locating free radio space than accessing partials in a better way, although I may be wrong. I'm sure it will have audio applications eventually...

Still the problem with any window-based FFT is that we have to get enough points (e.g. 512, 1024) before we can do the analysis, so there is always a delay (44100/1024 = ~43ms) between live input and output (latency). Much more interesting is the sliding phase vocoder (Russell Bradford, Richard Dobson, and John ffitch, 2005) where the FFT is adjusted in each sample rather than each frame, and the latency is significantly reduced. They also told me it is capable of things like FM synthesis with a live input as the carrier, although I haven't heard any examples of this (yet).

Ed


Reference:dream.cs.bath.ac.uk/Papers/spv-icmc2007.pdf

Gemnotes-0.1alpha: Live music notation for Pure Data
http://sharktracks.co.uk/


________________________________
From: Mathieu Bouchard <matju at artengine.ca>
To: Charles Henry <czhenry at gmail.com> 
Cc: pd-list at iem.at 
Sent: Wednesday, 25 January 2012, 22:25
Subject: Re: [PD] OT: faster than Fourier transform

Le 2012-01-25 à 16:16:00, Charles Henry a écrit :

> I've wondered about that too-from a perceptual point of view.  There's not much differentiation among noise signals.  They all mostly sound the same

Well, the brain still has to extract enough info from them to have a general idea of what kind of noise it is (a rough shape of spectrum), and ESPECIALLY extract the non-noise portions of the spectrum.

> Even though there's a very large number of dimensions for variation, they must have a sparse representation somewhere (in the brains... brains...)
> 
> but the FFT?  no way...

Well, there are perceptual models that try to separate «noise» from «non-noise» for easier encoding and stuff...

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