For each FFT bin (i.e. range of frequencies), the maximum value from all of the frames is kept as the feature. Continuing with the example above, we throw away 1/2 of every FFT (as it's simply a mirror image of the other half). We also throw away the bin at 0 Hz (as we filter out the DC bias anyway when we subtracted the mean), but we keep the Nyquist bin. As a result, we end up with 8 usable bins from each of our 16-point FFTs. For each bin, we find the maximum value from our 6 FFTs that we computed (in that particular bin). So, the number of features would be 8.