Yup.. my bad ;) . The sum terms are correct, but the formula was not (dyslexia or a few other bad traits must have been in control their ;) )

Hey NaN, here is a post I think you missed:

Also NaN, I believe you have said that the FFT decomposes an analog signal into a sum of sines and cosines. So in this case I can break down a WAV file into frequency domains and selectively amplifying the different frequency bands therefore emulating an EQ?

Also NaN just two small questions:

1. To your function do I pass the Integer Audio Samples (in the case of audio manipulation). I believe this is know as time domain data? Am I right?

2. What is represented by the Frequency Domain Data once your procedure is done? If you do not wish to answer I will check out the book that Pradeepan forwarded me too (Beginners Guid to DSP I think)

Thanks in advance NaN (or anyone else that provides an answer)

Also NaN, I believe you have said that the FFT decomposes an analog signal into a sum of sines and cosines. So in this case I can break down a WAV file into frequency domains and selectively amplifying the different frequency bands therefore emulating an EQ?

Also NaN just two small questions:

1. To your function do I pass the Integer Audio Samples (in the case of audio manipulation). I believe this is know as time domain data? Am I right?

2. What is represented by the Frequency Domain Data once your procedure is done? If you do not wish to answer I will check out the book that Pradeepan forwarded me too (Beginners Guid to DSP I think)

Thanks in advance NaN (or anyone else that provides an answer)

*Originally posted by x86asm*

Also NaN, I believe you have said that the FFT decomposes an analog signal into a sum of sines and cosines. So in this case I can break down a WAV file into frequency domains and selectively amplifying the different frequency bands therefore emulating an EQ?

Correct. This could be one approach to designing a descrete equalizer. However, the cost is there is alot more overhead converting to frequency domain, and back to time domain. There is other time domain filters that can also achieve this to some extent. However the frequencies are not isolated so discretely and as a result you get local attenuation or gain.

1. To your function do I pass the Integer Audio Samples (in the case of audio manipulation). I believe this is know as time domain data? Am I right?

Yes, an array of time domain data is given to the algorithm. The data should be paired with real and imagionary values (in time domain, you should have all zeros for imagionary). When the algorithm converst to frequency domain, these bins will be filled with each frequency bin's corresponding imagionary (phase) component for that frequency.

2. What is represented by the Frequency Domain Data once your procedure is done?

The data, as mentioned above, is paired with real and imagionary components. Any observable time domain data will only have real values (the imagionary bins will be 0's). In frequency domain, each array entry (real/imag) represents one frequency. The real component is the magnitude of that frequency bin, and the imagionary component is the phase for that frequency. (Re-read what i posted about imagionary numbers to help see this ~ Eulers identity absolute magnitude * e ^j(freq + Phase). ).

The FFT shows you Mag and Phase for each frequency. Also review your Fourier series theory. Basically, any given wave form (in time) can be represented as a sum of cos and sine frequencies at a specific magnitude and phase. So the FFT shows you essentially the DNA if you will of any given set of analog data, and is ASSUMED to repeat after the array is finished (which may not be true, but the time domain sample is all you give it).

Hope this helps...

:alright:

NaN