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I am looking at trying to achieve a 'realtime' (as quick as possible to acquire, process and present data) FFT application to analyse audio.

I have setup my app to acquire a set of samples, apply a 50% overlap window function, zero pad, compute FFT, extract magnitude and phase and display these. Currently I can do this with no issues, and minimal latency to the incoming signal.

However, as I have chosen a small sampling window (2048 samples) to achieve a fast time, I realise that this hinders resolution. I am also aware that there is only so much zero padding can achieve, if the acquired data is not there to begin with.

So my question is to do with how to achieve a better resolution for lower frequencies. It is my understanding that for accuracy, the time window should contain at least one cycle of the lowest frequency.

Is there a way that I can perform multiple FFTs on a signal with varying lengths and 'glue' the resultant FFT together. ie, a longer FFT will do the lower frequency range, and a shorter for the higher range. And if I can do this, will this retain phase information, or will I lose this completely?

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  • $\begingroup$ the term “glue” can mean many things. One can certainly perform multiple resolution DFTs on a signal and contrive ways to “paste” them together in some way meaningful to achieve some purpose but the way you asked, the question is not very specific $\endgroup$ – Stanley Pawlukiewicz Mar 19 at 16:27
  • $\begingroup$ Applogies. So I am looking at getting higher resolution in the lower frequencies. So to take different FFTs on the signal, and put them together in the frequency domain. Kind of band passing FFTs. I have just started reading about Wavelet Transform for continuous signals. I do not know if this is a red herring. But could I do Wavelet Transform for Frequency response to be ‘realtime’ and then perform a longer FFT to extract a stable phase response? $\endgroup$ – samp17 Mar 19 at 16:39
  • $\begingroup$ there are many ways to do filter banks. have you looked at the mel frequency cepstrum as an alternative. If you want to stick with DFTs, while bin resolution depends on DFT size, it is also of function of sample rate. Update rate depends on overlap. Is there a reason to retain phase? $\endgroup$ – Stanley Pawlukiewicz Mar 19 at 16:51
  • $\begingroup$ I would like to retain phase as I am using this to compare the phase shift of two signals. I have been reading more and it looks like constant q transform may be what I am after, as I do not need the high resolution in the higher frequencies. It all depends if I can get a fast enough algorithm for this. $\endgroup$ – samp17 Mar 20 at 12:34
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You can, and you don't need to zero pad, but a little overlapping does come into play.

Skipping the gnarly details and hitting this with a conceptual approach. Applies to two or more "levels".

Start with a longer frame for your low tones. The key is you need to smooth your signal fairly heavily. Lots of ways to do this. Then do a DFT (what a FFT really is) on the long interval. The reason for the smoothing is to eliminate alias frequencies at this level. The effects of your smoothing will be frequency dependent, so there are formulas for the different smoothing techniques. Do the inverse of the FFT of the smoothed signal with attenuation compensation applied. Subtract this from your original signal. You have now documented and removed your lower frequencies. Divide the interval into smaller chunks, and do DFTs as normal at the top level.

"Pasting" them together can also be done in different ways. Easiest is just to display one spectogram above the other as "display bands". You can also rescale the output from being linear in frequency to exponential if you want to put your results on an octave scale.

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  • $\begingroup$ Thanks, I have done a little bit more research, and it is my understanding that its the constant Q transform that I am after. It's fairly similar to the method you have given. As I am only interested in plotting the data on a logarithmic scale, I am happy to sacrifice the higher frequency resolution to gain some resolution in the low end. I am currently trying to follow the method on this page. doc.ml.tu-berlin.de/bbci/material/publications/Bla_constQ.pdf $\endgroup$ – samp17 Mar 20 at 12:32
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For a stationary spectral peak well above the noise floor and interference, look up phase vocoder frequency estimation techniques.

If a clear peak (maxima) appears in the same result bin in multiple overlapped FFT window, by using the change in phase in that FFT bin across successive offset windows (of known offset or overlap), a frequency estimate of higher resolution than the FFT bin spacing can often be acquired (again, depending on the S/N ratio).

Using the information from multiple overlapped windows is what allows the information gain to provide an improved frequency estimate, similar to the information gain of using a longer FFT. But you still get some trailing time resolution (if a frequency peak disappears from the last window). And if you run a phase vocoder backwards in time, you might also get some (non-real-time obviously) leading edge time resolution.

One problem with FFTs shorter than a few periods of the lowest frequency desired is that, near DC (or Fs/2), the side lobes from the conjugate mirror image (of any strictly real signal) is nearby, thus can leak in and distort phase estimation accuracy.

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