Clipping FFT Matrix

Answer 1

FFT's because they are windowed and sampled cause aliasing and sampling in the frequency domain as well. Filtering in the time domain is just multiplication in the frequency domain so you may want to just apply a filter which is just multiplying each frequency by a value for the function for the filter you are using. For example multiply by 1 in the passband and by zero every were else. The unexpected values are probably caused by aliasing where higher frequencies are being folded down to the ones you are seeing. The original signal needs to be band limited to half your sampling rate or you will get aliasing. Of more concern is aliasing that is distorting the area of interest because for this band of frequencies you want to know that the frequency is from the expected one.

The other thing to keep in mind is that when you grab a piece of data from a wave file you are mathmatically multiplying it by a square wave. This causes a sinx/x to be convolved with the frequency response to minimize this you can multiply the original windowed signal with something like a Hanning window.

Answer 2

It's worth mentioning for a 1D FFT that the first element (index [0]) contains the DC (zero-frequency) term, the elements [1:N/2] contain the positive frequencies and the elements [N/2+1:N-1] contain the negative frequencies. Since you didn't provide a code sample or additional information about the output of your FFT, I can't rule out the possibility that the "noisy power values at non-existent frequencies" aren't just the negative frequencies of your spectrum.

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EDIT: Here is an example of a radix-2 FFT implemented in pure Python with a simple test routine that finds the FFT of a rectangular pulse, [1.,1.,1.,1.,0.,0.,0.,0.]. You can run the example on codepad and see that the FFT of that sequence is

[0j,                    Negative frequencies
(1+0.414213562373j),    ^
0j,                     |
(1+2.41421356237j),     |
(4+0j),                <= DC term
(1-2.41421356237j),     |
0j,                     v
(1-0.414213562373j)]    Positive frequencies

Note that the code prints out the Fourier coefficients in order of ascending frequency, i.e. from the highest negative frequency up to DC, and then up to the highest positive frequency.