Assume a sequence of numbers (wave-like data). I perform then the DFT (or FFT) transform. Next step I want to achieve is to find the frequencies, that correspond to the real frequencies that are included in data. As we know, DFT output has real and imaginary part a[i] and b[i]. If we look at spectrum (sqrt(a[i]^2+b[i]^2) then the maximum in it corresponds to the frequency that is included to the data. The question is how to find all frequencies from DFT? The problem arises when there are many other peaks that can be falsely selected.
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I had a similar problem when doing spectral analysis processing of data when I was writing my honours thesis.
You are right: To find dominant frequencies you generally only need to look at the magnitude of the complex value in the DFT.
Unfortunately, you pretty much have to write some sort of intelligent algorithm which will identify the peaks (frequencies). The way the algorithm works is highly dependent on what the DFT looks like for your application. My DFTs all had similar characteristics, so it wasn’t too difficult to put together a heuristic algorithm. If your DFT can take on any form, then you will probably get a lot of false positives and/or false negatives.
The way I did it was to identify regions in the DFT with high magnitude (peaks) which were surrounded by low magnitude (troughs). You can define the minimum difference between peaks and troughs (the sensitivity) as a constant times the standard deviation of the data. Additionally, you can say that any peaks that fall below a certain magnitude (threshold) are ignored altogether, as they are just noise.
Of course, the above technique will only really work if you have relatively well defined frequencies in your data. If your DFT is highly random, then you will need to take extra care to set the sensitivity and threshold carefully.
Don’t forget that the magnitude of your data is symmetric, so you only need to look at half of it.
Once you have identified the frequencies in your DFT, don’t forget to convert it into the units you want. From memory, if you have n samples taken with time discretisation dt, then if you have a peak at data point 5 (for example), where the first data point is 1, then the frequency is 1/(n*dt) radians per time unit. (I haven’t done this in a while, so that formula might be off by a factor of Pi or something)