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In the middle of the notch is a 1000Hz pure tone and in the middle of the other masking noise (with no bandstop filter) there is also a 1000Hz pure tone. SO I think the energy between the upper and lower limit of the filter will be removed. Actually the 2nd one is a bandstop filter. Same with Bandpass filter (1000 to 2000 Hz): Or in other words, you will see that significant values also remains at p for L < n < U. As far as the FFT values are concerned, this simply means there will be some index L for which all p with n U become zero. Only frequencies between the filter's lower and upper bounds remain - probably with some "fade in" and "fade out" near the boundaries. And all frequencies above the filter's upper bound will be removed too. In other words, all frequencies below the filter's lower bound will be removed. Now, if you apply a "bandpass" filter on such a signal, it will cancel out the frequencies outside of the filter's range. Therefore, if you compute the FFT values p, as explained in my previous post, for such a "white noise" input signal, then you'd expect that all your values p, for n in the 0 to (N/2)-1 range, have pretty much the same absolute values - with a certain fluctuation over time, of course. Anyway, if we assume that your original input signal was "white noise" (which is always good for testing), then - without any filter applied, you would expect that all frequencies have approximately the same magnitude. Well, what you are going to see in your FFT analysis greatly depends on what your original input signal was, not only on what filter you apply. For example, if your input data was a simple Sine wave, you would notice that only a single frequency will have a significant value (and the index of p where you have that peak indicates the frequency of the Sine), while all other frequencies are near-zero (they are not exactly zero due to the spectral leakage). This is the "spectrum" of your input data. Put simply, the value p is the magnitude of the n-th frequency. So if r is the real part of the n-th FFT value and i is the imaginary part of the n-th FFT value, what you actually want to look at is p = sqrt(r^2 + i^2), where "sqrt" is the square root. Those FFT values are complex though! Usually you are interested in the signal magnitudes. This means, if you use, for example, frames of size 2048 samples, you effectively get 1024 FFT coefficients per frame. So you only need to look at the first N/2 FFT values. That's because the second N/2 values are just the first N/2 values mirrored (because your input was real-valued). However, you actually only have N/2 FFT values.
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Now, how to interpret the FFT data: Computing the FFT of a frame with a size of N samples gives you N FFT coefficients. I would highly recommend to not implement FFT yourself (except for educational purposes), but instead use the FFTW library for that purpose. Finally, you compute the FFT of each frame. This is done by simply multiplying the i-th sample in the frame with the i-th weight of the window function. Next, you usually apply some kind of window function to each frame, e.g. Typical frame sizes are powers of two, like 1024, 2048 or 4096 samples per frame. If you start with the "raw" PCM data, you will usually cut it into fixed size "frames" first. Does anyone have a consultant they can recommend> I am willing to pay a consultant fee. Is there anyone out there who can help me run an FFT analysis and help me to interpret what I am seeing? I know what I need to see but I am not sure how to get to that analysis and I am not 100% sure of what I am looking at. LSylvia wrote:I have files that contain the output of stimuli of an experiment that I am running.