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Signal Processing

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  • $\begingroup$ That's not universally true – different DFT implementations apply different normalizations. That's why my answer recommends simply trying out what your DFT does. $\endgroup$ Commented Mar 19, 2019 at 20:50
  • $\begingroup$ @MarcusMüller I see. I had trouble understanding the last paragraph of your answer because I'm not familiar with the terms ADC or DC. I also wasn't sure exactly what you meant by " a constant vector of $N$ maximum values." Does this mean a vector in which every element is the maximum possible value? I haven't been studying signal processing concepts for very long, so I definitely have some pretty major knowledge gaps. $\endgroup$ Commented Mar 19, 2019 at 21:58
  • $\begingroup$ Does this mean a vector in which every element is the maximum possible value? Yes, that was exactly what I wanted to say :) $\endgroup$ Commented Mar 19, 2019 at 22:01
  • $\begingroup$ @MarcusMüller Interesting idea! I tried creating a vector that looks like $[32768 \text{ } 32768 \text{ .. } 32768]$ and the FFT result I got back was all zeroes except for the first bin, which had value $134201344$. I suppose this would be the maximum possible FFT output without any scaling? If I scale the FFT result according to how I described in my answer, the first bin contains $32764$. Any idea why it is this instead of $32768$, which would be what I'd expect? $\endgroup$ Commented Mar 19, 2019 at 22:15
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    $\begingroup$ yep, that's right, based on FFTW, but doing its own normalization! (ifft(fft(x)) == x in Matlab, but in FFTW, it's x·N, iirc.) $\endgroup$ Commented Mar 20, 2019 at 15:32