FFT on WP7 shows two mirrors

Hello I'm exploring the audio possibilities of the WP7 platform and the first stumble I've had is trying to implement a FFT using the Cooley-Tukey method. The result of that is that the spectrogram shows 4 identical ima开发者_Python百科ges in this order: one normal, one reversed, one normal, one reversed. The code was taken from another C# project (for desktop), the implementation and all variables seem in place with the algorithm. So I can see two problems right away: reduced resolution and CPU wasted to generate four identical spectrograms. Given a sample size of 1600 (could be 2048) I know have only 512 usable frequency information which leaves me with a 15Hz resolution for an 8kHz frequency span. Not bad, but not so good either.

Should I just give up on the code and use NAudio? I cannot seem to have an explanation why the spectrum is quadrupled, input data is ok, algorithm seems ok.

This sounds correct. You have 2 mirrors, I can only assume that one is the Real part and the other is the Image part. This is standard FFT.

From the real and image you can compute the magnitude or amplitude of each harmonic which is more common or compute the angle or phase shift of each harmonic which is less common.

Gilad.

I switched to NAudio and now the FFT works. However I might have found the cause (I probably won't try to test again): when I was constructing an array of double to feed into the FFT function, I did something like:

            for (int i = 0; i < buffer.Length; i+= sizeof(short))
            {
                samples[i] = ReadSample(buffer, i);
            }

For reference, 'samples' is the double[] input to fft, ReadSample is something that takes care of little/big endian. Can't remember right now how the code was, but it was skipping every odd sample.

My math knowledge has never been great but I'm thinking this induces some aliasing patterns which might in the end produce the effect I experienced.

Anyway, problem worked around, but thanks for your input and if you can still explain the phenomenon I am grateful.