Smearing an audio recording using Fourier transform

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SUMMARY

The discussion focuses on techniques for smearing an audio recording using the Fourier transform, specifically through the Short-Time Fourier Transform (STFT). The user seeks to achieve a sound similar to a heavily reverberated version of a piano melody, aiming to average the frequency content over time. They explore the relationship between the Discrete Fourier Transform (DFT) and the time-varying nature of audio signals, ultimately questioning the purpose of these transformations in audio processing. The user also shares experimental results using inverse Fourier transform techniques to achieve a smeared effect.

PREREQUISITES
  • Understanding of Fourier Transform and its applications in audio processing
  • Familiarity with Short-Time Fourier Transform (STFT) concepts
  • Knowledge of audio effects, particularly reverb and decay settings
  • Experience with audio editing software capable of performing Fourier analysis
NEXT STEPS
  • Research techniques for implementing Short-Time Fourier Transform (STFT) in audio processing
  • Explore advanced reverb algorithms and their impact on audio smearing
  • Learn about the inverse Fourier Transform and its applications in audio synthesis
  • Investigate methods for averaging frequency content in audio signals
USEFUL FOR

Audio engineers, sound designers, and musicians interested in advanced audio manipulation techniques and the application of Fourier transforms in creating unique soundscapes.

dwarp
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Hi!

I'd like to smear an audio recording, where the frequency content audibly changes, into an audio recording where it does not. Here's a recording of a sampled piano playing a melody, which will serve as an example:

https://dl.dropboxusercontent.com/u/9355745/oldmcdonald.wav

The frequency content changes, both during each note played and because different notes are being played. I'd like to use the Fourier transform to somehow produce something like this:

https://dl.dropboxusercontent.com/u/9355745/oldmcdonaldsmear.wav

This was created by repeatedly playing the original recording into a reverb with a very high decay. There's still some "shimmering" in the recording, so the result isn't completely smeared out.

One way I can think of doing this is by computing an STFT and then combining the resulting windows into an average, which is then used as input to the reverse Fourier transform to produce a new audio recording (and I'm guessing this is what the reverb is actually doing). Is there a simpler, perhaps more elegant way of doing this?

The reason I'm asking is that for the longest time, I thought the result of the DFT *was* the average frequency content of the input. It seems this is only true if the frequency content in the input signal does not change over time - if I had been able to smear the signal completely, the DFT of that smeared signal *would* have been the average frequency content of the smeared signal. Imagine, for instance, if I'd run 4 seconds of a square wave at 80 Hz - the DFT would give me the same thing an EQ analyzer would.

But then, since the un-smeared signal can be accurately represented by a sum of sine and cosine waves (that is, the result of the DFT), its frequency content does not, in fact, change over time. Obviously, though, if you listen to the unsmeared signal, its frequency content DOES change over time, or there wouldn't be a melody! I find all this incredibly confusing.
 
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What is the purpose for such a process? Could you for instance, under perfect conditions, go backwards and end up with the same notes in the right sequence?
 
The purpose is getting an idea of the frequency content in an audio recording - whether there's a lot of bass, a lot of treble, etc. I also like the idea of getting the "average sound" of a longer recording just to hear what it sounds like. I don't expect to be able, and am not interested in being able, to reproduce the original signal from the result, as I would be able to with a straight up DFT, no.
 
Also, since I believed the DFT contained the average frequency content, I did try ifft(abs(fft(signal), and I actually got kind of close to what I was trying to achieve - the result is kind of smeared - but it's also still "pulsating" (the volume is modulating) at the same frequency that the notes were played in the original recording (4 Hz):

https://dl.dropboxusercontent.com/u/9355745/newmcdonald.wav
 
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