Earbuds.
AKG K 314 P
Some visual seam issues in the graphs, but nothing major - I'll sort it out some day. Colors remain unchanged for now.
Earbuds.
AKG K 314 P
Some visual seam issues in the graphs, but nothing major - I'll sort it out some day. Colors remain unchanged for now.

A tone starts and stops - why wouldn't it? But what you get from pushing a single cycle through FFT (what I assume Audacity is giving out) is a rough idea of where in frequency that energy is. How the decay (number of cycles) is calculated in those types of individual decay plots, I don't know.
What I mean is, if a tone stops abruptly, that's a broadband phenomenon. I looked at the FFT of a single cycle of a 1000Hz tone with silence before and after it, and it looks nothing like the FFT of a continuous 1000Hz tone. So a tone that stops abruptly would be testing a phone's decay in all frequencies not just the frequency the tone was at.
If you've got a single cycle of an x kHz sine wave and you run it through FFT, you'll either have very poor resolution (1 millisecond for one 1 kHz cycle) or better (but not perfect) resolution with additional silent samples but still only 1 ms with actual sound data. But look at the amplitude plot with your eyes and you can easily determine the exact frequency of the sine wave + where it starts and stops. (I'm not at all sure what you mean, still.)
There are a couple of problems with this, two obvious ones being that a 1 kHz sine wave sampled at 44.1 kHz requires 44.1 samples for a single cycle and another being that if you zero-pad, which you have in order to get a usable result, the FFT won't like the discontinuity. The result will be anything but a single peak at 1 kHz.
Two vintage on-ears.


Case 1) Tone corresponds to an FFT bin and window is achieved by zero padding in the time domain:
If one applies a rectangular window to a tone in the time domain (meaning the tone abruptly starts and stops) then that corresponds, in the frequency domain, to convolving the FFT of a rectangular window (a sinc function) with the tone (a delta/single spike if the tone corresponds to an FFT bin.) Since the tone in this case is a delta, the result in the frequency domain is a sinc function whose peak occurs at the tone frequency. The lobe's width of the sinc is proportional to the size of the window. The larger the window (the more tone cycles in the time domain), the narrower the sinc...
Case 2) Tone does not correspond to an FFT bin:
One will get side lobes (instead of an delta in the frequency domain) when calculating the FFT of a tone, if the tone does not correspond to any of the FFT bin frequencies. Worst case scenario is if the tone frequency is half way between bins...
Hope this helps.
FFT is kinda like the effect they use in certain films to blur out certain parts. Lower resolution = can't make out the fine details. I can't see how FFT having a weakness of resolution shows a fundamental difference between one cycle and many cycles in this case... Unless it's in the context of measurement or something.
I had a quick google on that and apparently we're talking about artifacts of digital sampling?

FFT is kinda like the effect they use in certain films to blur out certain parts. Lower resolution = can't make out the fine details. I can't see how FFT having a weakness of resolution shows a fundamental difference between one cycle and many cycles in this case... Unless it's in the context of measurement or something.
DFT is an invertible, discrete, linear mapping. There is no blurring. Zero-padding doesn't increase resolution, it just causes a higher interpolation-density in the other domain. You have to use more data (more cycles) to increase resolution.
So the resolution will be quite bad with a single cycle and windowing a single cycle will probably make the result unusable unless you use a very low dynamic range window function. It is the discontinuity (abrupt start and stop) that causes the undesirable effects we see. Like ultrabike wrote, it's like using a rectangular window.
What you could do is record a x kHz sine wave that stops abruptly and look at the last cycle and post-ringing in the time domain.
When I used the word blur, I referred (clumsily) to the effect of pixelation, where you decrease the resolution of an image, increase it back up again and end up with a low-resolution version of the original picture - and it's akin to this that you get as output from FFT. I.e. frequency bins that span some given range of frequencies, dependent on the sampling size, and thus you get a low-resolution 'pixelated' 'image' of the frequency distribution, akin to a view of a crotch in a bashful film.
If you get an abrupt start and stop, surely that's a result of aliasing in sampling and not a physical property of a sine wave? I couldn't quite unpack the jargon in arnaud's explanation...
vid, have a read here if not already done, in particular the explanation on how Square Waves are constructed: http://www.innerfidelity.com/content/headphone-measurements-explained-square-wave-response . The same reasoning can be used to understand the broad frequency content of any abrupt transition, including the rectangular window necessary to stop the pure tone from ringing after 1 cycle.