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I am willing to accept that "audio engineering is much more complex" than I seem to think it is, and because of your comment I will look more closely into thing

I am willing to accept that "audio engineering is much more complex" than I seem to think it is, and because of your comment I will look more closely into things before making any claims like that again. Would you be willing to explain in more detail the "good reasons why they're not a one-size-fits-all solution for audio effects"?

dspig 12y ago
A big reason against FFT is latency - for good resolution at low frquencies you need a big FFT, so it takes longer to buffer that many samples before you can FFT them. Also a lot of the processes we want to apply to audio have much more efficient methods available than FFT or convolution such as IIR filters - which also have no latency apart from whatever phase shift they introduce. Or if the process is time varying or has embedded non-linearities then it lends itself to sample-by-sample processing and not block based like FFT and (efficient) convolution.
chipsy 12y ago
Some examples... The reverberation algorithms mentioned by TheOtherHobbes often bear no resemblance to the methods used for analog gear. The most commonly understood setup, in the first digital reverbs described by Manfred Schroeder [0], uses banks of all-pass delay lines set up in "aesthetically interesting" sequence and series, employing various levels of feedback. Digital frequency filters (and I'm pretty unclear on all the details myself, not having enough DSP background) generally apply the Z-transform [1] to turn a filter specification into a fast state-variable algorithm. This technique is crucial to modelling certain aspects of analog audio since it allows the filter to behave in more chaotic, animated ways: for example, resonance in analog domain is achieved through filter feedback, which causes the filter to self-oscillate at high gain levels. Resampling algorithms contain some of the most straightforward applications of Fourier transforms. The theory tells us to treat each sample of the original signal as a point convolved by the sinc function; the resulting output signal is resampled from this convolution, and is thus dependent on the _bandwidth_ of the convolution - as bandwidth goes up, our convolution grows wider, as it goes down, shorter. The bandwidth modulation guarantees that the output signal contains frequencies underneath the Nyquist limit. However, in practical use, we immediately have to compromise by at least windowing the sinc; thus we get into various approximations to minimize artifacts. Resampling can become a distinctive effect when done with lower fidelity or unusual filter kernels(example: The SPU of the Super Nintendo employs a Gaussian kernel). [0] http://valhalladsp.wordpress.com/2009/05/30/schroeder-reverbs-the-forgotten-algorithm/ http://valhalladsp.wordpress.com/2009/05/30/schroeder-reverb... [1] http://en.wikipedia.org/wiki/Z-transform http://en.wikipedia.org/wiki/Z-transform

dspig 12y ago

A big reason against FFT is latency - for good resolution at low frquencies you need a big FFT, so it takes longer to buffer that many samples before you can FFT them. Also a lot of the processes we want to apply to audio have much more efficient methods available than FFT or convolution such as IIR filters - which also have no latency apart from whatever phase shift they introduce. Or if the process is time varying or has embedded non-linearities then it lends itself to sample-by-sample processing and not block based like FFT and (efficient) convolution.

chipsy 12y ago

Some examples... The reverberation algorithms mentioned by TheOtherHobbes often bear no resemblance to the methods used for analog gear. The most commonly understood setup, in the first digital reverbs described by Manfred Schroeder [0], uses banks of all-pass delay lines set up in "aesthetically interesting" sequence and series, employing various levels of feedback. Digital frequency filters (and I'm pretty unclear on all the details myself, not having enough DSP background) generally apply the Z-transform [1] to turn a filter specification into a fast state-variable algorithm. This technique is crucial to modelling certain aspects of analog audio since it allows the filter to behave in more chaotic, animated ways: for example, resonance in analog domain is achieved through filter feedback, which causes the filter to self-oscillate at high gain levels. Resampling algorithms contain some of the most straightforward applications of Fourier transforms. The theory tells us to treat each sample of the original signal as a point convolved by the sinc function; the resulting output signal is resampled from this convolution, and is thus dependent on the _bandwidth_ of the convolution - as bandwidth goes up, our convolution grows wider, as it goes down, shorter. The bandwidth modulation guarantees that the output signal contains frequencies underneath the Nyquist limit. However, in practical use, we immediately have to compromise by at least windowing the sinc; thus we get into various approximations to minimize artifacts. Resampling can become a distinctive effect when done with lower fidelity or unusual filter kernels(example: The SPU of the Super Nintendo employs a Gaussian kernel). [0] http://valhalladsp.wordpress.com/2009/05/30/schroeder-reverbs-the-forgotten-algorithm/ http://valhalladsp.wordpress.com/2009/05/30/schroeder-reverb... [1] http://en.wikipedia.org/wiki/Z-transform http://en.wikipedia.org/wiki/Z-transform