If you look at the vertical scale, you will notice that in both cases they are going down to around -90dB.
One way to think of the FFT algorithm (on which the spectrum plot is based), is that it splits the full frequency range into a number of equally spaced “bins”. So, as a silly but easy example:
- If the sample rate is 1600 Hz, then the frequency range is 0 to 800 Hz (from DC, up to half the sample rate)
- If the “FFT Size” is 16, then the number of “bins” will be 8 (the number of frequency bins is half the “Size”)
- Each “bin” catch frequencies that are within equally spaced frequency ranges…
- The frequency ranges will be: 0-100, 100-200, 200-300, 300-400, 400-500, 500-600, 600-700, and 700-800Hz
- The signal is divided between these 8 bins according to the frequency components of the signal
If, for example, the signal in this scenario was white noise, then we would expect the same “amount” to be in each bin.
Now what would happen if we have twice as many bins? Each bin would now cover a frequency range half the size, so in the above example, rather than catching frequencies that lie with a 100 Hz range, each bin would now catch frequencies that are within a 50 Hz range, so as we would reasonably expect, they will usually catch “less stuff”. In the case of white noise, for an FFT Size of 32, there will be 16 bins, so each bin will now catch only 1/16 of the noise rather than 1/8th of the noise.
One final point about this: If the signal contains a pure sine tone, then no matter how small the bins are, the entire signal should fit into one bin. (In practice it is more complicated because there is ‘leakage’ between bins, and there are edge cases where a tone may be on the boundary of two bins).
The measurements (vertical scale) in Plot Spectrum is such that a 0 dB sine wave should produce a 0 dB peak.
Back to your case.
With increasing FFT Size, the bins are smaller, so each bin catches less. The problem in your second (Size = 16384) image is that the high frequency bins are catching so little that they are off the bottom of the scale (less than -90 dB).
If you are not interested in frequencies below, say, 5kHz, then you could use the Equalization effect to reduce those unwanted frequencies (a lot), and then amplify the remaining signal. That will hopefully bring those high frequencies into range for Plot Spectrum. (I’ve just got my fingers crossed that the “magical auto scaling” does not mess this up for you). Try this, and post a screen shot to let us know how it goes.
