Why does the Hamming window attenuate the FFT? I am working on a very simple voice recognition system using the msp430. In my research I've read that for real life audio signals its necessary to apply a window to the FFT data because the audio signals won't be periodic. In my code, I've applied the hamming window and plotted it against the non-windowed FFT spectrum. It appears that the hamming attenuates the signal in the lower valued N data points.



I think this makes sense based upon the equation of the hamming window, but why would I want this attenuate for my signal? It seems that identifying the spectral characteristics would be more difficult after the window. Any thoughts?
You can figure the change in energy applied to your signal by analyzing the energy in your window.  The Hamming window's energy (or scaling) is well known and Google will tell you what it is.  You are doing something different. Can you post your code for further help?

From your code, ans your image, it really appears that you applied the window to your transformed data.  The window (in this case) is applied to the time data before the transform.  The window will reduce sidelobes from strong frequency components.  Take note that taper windows cause the so-called "spectral leakage" where energy leaks into other frequency bins