fft or rta




We get questions about whether its better to download our RTA app, or FFT. And, since Apple does not allow trying out apps, it can make the decision that more difficult. This page will try to sort out the differences for you, so that you can make an informed choice.

Both are great apps, and both give you information about frequency level, but there are differences.

Also, see our “dB vs. dB” page to understand what the SPL numbers mean, and why they might be different on different modules.

Difference #1: RTA is octave (or 1/3 octave) based

RTA works by literally running 10 ANSI octave band filters, or 30 ANSI one-third octave band filters on the input signal in real time. This means that even if there is just one sine wave at 1000 Hz at 85dB, the 1000 Hz octave or 1/3 octave band will show 85dB. Every other sine wave that fits in that band could be at 84.9dB, or even 85dB, and the bar would not be any higher.

That’s not so great when you are trying to EQ a room with a tone generator, but when pink noise or program material (music) is playing, it’s really quite useful. And, there are many cases where various acoustical tests and standard are looking for 1/3 or octave band measurements.

FFT on the other hand has much finer resolution, just a few Hz in some cases. Depending on the smoothing setting in FFT, you can see all of the individual frequency components. And this brings us to number 2:

Difference #2: RTA is easier and faster to use

RTA has very few parameters to set: octave or 1/3 octave, and how fast do you want the display to react. This can be a huge advantage in a live situation, or when you don’t have a lot of time to spend tweaking parameters, not to mention the learning curve that is required to really get the most out of FFT.

FFT has settings for FFT size (128 to 16384 points, and equal points per octave), smoothing (none, 1/24th 1/12th, 1/6, 1/3, octave), and decay speed (0.5 to 8 seconds, peak, average), which all interact. Although you can get useful information without knowing much about these, you will get much more information from the app if you do know your way around.

Difference #3: Curve overlays

RTA has overlay curves that can be selected. These include various noise standards, such as NCB, NR, or PNC. These overlays are not present in FFT. Why? Because all of the noise standards are written specifically for octave or 1/3 octave band filters, which RTA has and FFT does not. So, although we could add them, they would not be technically correct.

Difference #4: Max & Min bars

Again only in RTA. This option, when turned on, keeps track of the highest and lowest levels that a bar has reached since resetting it (by double-tapping the screen). If we did this in FFT it would just clutter the screen.

Difference #5: Peak tracking

FFT has an option to turn on peak tracking, which will show the exact value of loudest current frequency. RTA does not have this, since the bands are too wide to show an exact frequency.

Difference #6: Frequency scale zooming

FFT has frequency scale zooming. Pinch to zoom in or out, down to a single octave. RTA does not have this since their is no more detail to see by zooming. FFT can have much more detail showing..

Difference #7: Display multiple curves

FFT can also show a stored curve alongside a running FFT curve, for visual comparison. Again RTA will only display one stored curve.

Difference #8: RTA bar levels vs. smoothed FFT levels

This one is a little tricky to explain. Think of a 1000 Hz sine wave that reads 80dB on an SPL meter. This will always show an 80dB bar in RTA, whether in octave or 1/3 octave more. If you think about the filters in an RTA as “gates”, that let through certain frequencies while stopping others, if the frequency is within the gate, it gets through un-attenuated, and will show its full level.

Now, in FFT we have smoothing. This is a little different. The definition of this type of smoothing is that for say, 1/3 octave smoothing, any given point is averaged algorithmically with the 1/6 octave above and below it. The effect of this is a nice smooth graph, but peaks (and valleys) get less sharp. So, the 80dB 1000 Hz sound in our example will show a value lower than 80dB. Again, this is great to see the overall picture, but it can lead to confusion, and will give a different curve than RTA, with exactly the same signals.

OK, which one is better?

It’s “horses for courses” as they say in the UK, FFT certainly has more resolution and options, but RTA is simpler, quicker, and often more than enough data to solve the problem. Of course, our answer is: Why not get them both?