# When should I calculate PSD instead of plain FFT magnitude spectrum?

I have a thirty-second speech signal that was sampled at 44.1 kHz. Now, I would like to show what frequencies the speech has. However, I'm not sure what would be the best way to do that. It seems sometimes one calculates the absolute value of a Fourier transform, and sometimes power spectral density. If I understand correctly, the latter works so that I divide my signal into parts, do FFT part-by-part and somehow sum these. Window functions are somehow involved. Can you clarify this a bit for me? I'm new to DSP.

• Breaking the signal up into segments, finding the spectrum of each, and then averaging the spectra can help to reduce noise, but reduces resolution, too. see en.wikipedia.org/wiki/Welch%27s_method – endolith Jul 25 '13 at 19:43

In practice, you can compute the PSD as simply the absolute magnitude of the fourier transform squared. For example, if your signal is $x[n]$, and its DFT is $X(f)$, then the absolute magnitude of the DFT is $|X(f)|$, while the PSD is $|X(f)|^2$.