Am I using the Fourier transformation the right way?

Question

I am wondering if I am using Fourier Transformation in MATLAB the right way. I want to have all the average amplitudes for frequencies in a song. For testing purposes I am using a free mp3 download of Beethovens "For Elise" which I converted to a 8 kHz mono wave file using Audacity.

My MATLAB code is as follows:

clear all % be careful

% load file
% Für Elise Recording by Valentina Lisitsa 
% from http://www.forelise.com/recordings/valentina_lisitsa
% Converted to 8 kHz mono using Audacity
allSamples = wavread('fur_elise_valentina_lisitsa_8khz_mono.wav');


% apply windowing function
w = hanning(length(allSamples));
allSamples = allSamples.*w;


% FFT needs input of length 2^x
NFFT = 2^nextpow2(length(allSamples))


% Apply FFT
fftBuckets=fft(allSamples, NFFT); 
fftBuckets=fftBuckets(1:(NFFT/2+1)); % because of symetric/mirrored values


% calculate single side amplitude spectrum, 
% normalize by dividing by NFFT to get the 
% popular way of displaying amplitudes
% in a range of 0 to 1
fftBuckets = (2*abs(fftBuckets))/NFFT; 

% plot it: max possible frequency is 4000, because sampling rate of input
% is 8000 Hz
x = linspace(1,4000,length(fftBuckets));
bar(x,fftBuckets);

The output then looks like this:

1. Can somebody please tell me if my code is correct? I am especially wondering about the peaks around 0.
2. For normalizing, do I have to divide by NFFT or length(allSamples)?
3. For me this doesn't really look like a bar chart, but I guess this is due to the many values I am plotting?

Thanks for any hints!

Walkabout

Have you seen this page: Using FFT to Obtain Simple Spectral Analysis Plots. It has an example with plenty of explanations – Proletarian

Answer 1

1. Depends on your definition of "correct". This is doing what you intended, I think, but it's probably not very useful. I would suggest using a 2D spectrogram instead, as you'll get time-localized information on frequency content.

2. There is no one correct way of normalising FFT output; there are various different conventions (see e.g. the discussion here). The comment in your code says that you want a range of 0 to 1; if your input values are in the range -1 to 1, then dividing by number of bins will achieve that.

3. Well, exactly!

I would also recommend plotting the y-axis on a logarithmic scale (in decibels), as that's roughly how the human ear interprets loudness.

Dam

Answer 2

Two things that jump out at me:

1. I'm not sure why you are including the DC (index = 1) component in your plot. Not a big deal, but of course that bin contains no frequency data
2. I think that dividing by length(allSamples) is more likely to be correct than dividing by NFFT. The reason is that if you want the DC component to be equal to the mean of the input data, dividing by length(allSamples) is the right thing to do.

However, like Oli said, you can't really say what the "correct" normalization is until you know exactly what you are trying to calculate. I tend to use FFTs to estimate power spectra, so I want units like "DAC / rt-Hz", which would lead to a different normalization than if you wanted something like "DAC / Hz".

Ultimately there's no substitute for thinking about exacty what you want to get out of the FFT (including units), and working out for yourself what the correct normalization should be (starting from the definition of the FFT if necessary).

You should also be aware that MATLAB's fft has no requirement to use an array length that is a power of 2 (though doing so will presumably lead to the FFT running faster). Because zero-padding will introduce some ringing, you need to think about whether it is the right thing to do for your application.

Finally, if a periodogram / power spectrum is really what you want, MATLAB provides functions like periodogram, pwelch and others that may be helpful.

Lardon