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933144
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2009-05-31T23:49:36.443
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2009-06-01T02:56:44.473
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<p>FFT's because they are windowed and <a href="http://en.wikipedia.org/wiki/Sampling_(signal_processing)" rel="nofollow noreferrer">sampled</a> cause <a href="http://en.wikipedia.org/wiki/Aliasing" rel="nofollow noreferrer">aliasing</a> 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 <a href="http://en.wikipedia.org/wiki/Nyquist%E2%80%93Shannon_sampling_theorem" rel="nofollow noreferrer">original signal needs to be band limited to half your sampling rate</a> or you will get <a href="http://en.wikipedia.org/wiki/Aliasing" rel="nofollow noreferrer">aliasing</a>. 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. </p> <p>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 <a href="http://en.wikipedia.org/wiki/Window_function" rel="nofollow noreferrer">Hanning window</a>. </p>
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1. POClipping FFT Matrix
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1. USRex Logan
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1. USRex Logan
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