Applying Fourier to a real signal: guitar musical note B

I recorded a sound from the second string of my electric guitar (musical note B) and made a .wave file out of it. By using Matlab I tried to separate the sound of the note from the background noise and made some experiments.

At first I wanted to use Python for doing this, since I’m more knowledgeable about that than Matlab, however it looks like the most common ways to open a .wave file (scipy.audiolab and similar) are not available for Python 3 yet (either that or I could not find anything). Matlab provides simple functions for handling .wave files and great graphing tools but I could not find something to convert bins into frequencies as the function in numpy does, therefore I’m not sure the frequencies in the graph are correct.

Anyway, you can download the recording at this link, and here is the time domain signal representation:

By taking the FFT of the signal and filtering out the frequencies we don’t need, we filter out the noise and/or sounds we don’t need. My filtering rule lets through all the frequencies above 200 and below 1600 Hz. Of course this is a very naive way of filtering, however that’s a start.

You can play around and block out one, two, three peaks a time and hear the difference between the original recording and the edited one. Now, ideally B of the guitar should have a frequency of about 249 Hz (more or less), however it looks like the frequencies between 650 and 1020 Hz in our frequency domain representation are the main components of the note. Perhaps that’s because I messed up the bins. Anyway I’m glad that by applying a so simple filtering rule I’ve been able to remove the pitch sound at the beginning and smoothing out the musical note. Finally I also found out that Matlab has great publishing tools that enable you to print out an HTML file of your code, the results and graphs all together. Here is the code I used: