Python – Analyze audio using Fast Fourier Transform

audiofftpythonsignal-processingspectrum

I am trying to create a graphical spectrum analyzer in python.

I am currently reading 1024 bytes of a 16 bit dual channel 44,100 Hz sample rate audio stream and averaging the amplitude of the 2 channels together. So now I have an array of 256 signed shorts. I now want to preform a fft on that array, using a module like numpy, and use the result to create the graphical spectrum analyzer, which, to start will just be 32 bars.

I have read the wikipedia articles on Fast Fourier Transform and Discrete Fourier Transform but I am still unclear of what the resulting array represents. This is what the array looks like after I preform an fft on my array using numpy:

   [ -3.37260500e+05 +0.00000000e+00j   7.11787022e+05 +1.70667403e+04j
   4.10040193e+05 +3.28653370e+05j   9.90933073e+04 +1.60555003e+05j
   2.28787050e+05 +3.24141951e+05j   2.09781047e+04 +2.31063376e+05j
  -2.15941453e+05 +1.63773851e+05j  -7.07833051e+04 +1.52467334e+05j
  -1.37440802e+05 +6.28107674e+04j  -7.07536614e+03 +5.55634993e+03j
  -4.31009964e+04 -1.74891657e+05j   1.39384348e+05 +1.95956947e+04j
   1.73613033e+05 +1.16883207e+05j   1.15610357e+05 -2.62619884e+04j
  -2.05469722e+05 +1.71343186e+05j  -1.56779748e+04 +1.51258101e+05j
  -2.08639913e+05 +6.07372799e+04j  -2.90623668e+05 -2.79550838e+05j
  -1.68112214e+05 +4.47877871e+04j  -1.21289916e+03 +1.18397979e+05j
  -1.55779104e+05 +5.06852464e+04j   1.95309737e+05 +1.93876325e+04j
  -2.80400414e+05 +6.90079265e+04j   1.25892113e+04 -1.39293422e+05j
   3.10709174e+04 -1.35248953e+05j   1.31003438e+05 +1.90799303e+05j...

I am wondering what exactly these numbers represent and how I would convert these numbers into a percentage of a height for each of the 32 bars. Also, should I be averaging the 2 channels together?

Best Solution

The array you are showing is the Fourier Transform coefficients of the audio signal. These coefficients can be used to get the frequency content of the audio. The FFT is defined for complex valued input functions, so the coefficients you get out will be imaginary numbers even though your input is all real values. In order to get the amount of power in each frequency, you need to calculate the magnitude of the FFT coefficient for each frequency. This is not just the real component of the coefficient, you need to calculate the square root of the sum of the square of its real and imaginary components. That is, if your coefficient is a + b*j, then its magnitude is sqrt(a^2 + b^2).

Once you have calculated the magnitude of each FFT coefficient, you need to figure out which audio frequency each FFT coefficient belongs to. An N point FFT will give you the frequency content of your signal at N equally spaced frequencies, starting at 0. Because your sampling frequency is 44100 samples / sec. and the number of points in your FFT is 256, your frequency spacing is 44100 / 256 = 172 Hz (approximately)

The first coefficient in your array will be the 0 frequency coefficient. That is basically the average power level for all frequencies. The rest of your coefficients will count up from 0 in multiples of 172 Hz until you get to 128. In an FFT, you only can measure frequencies up to half your sample points. Read these links on the Nyquist Frequency and Nyquist-Shannon Sampling Theorem if you are a glutton for punishment and need to know why, but the basic result is that your lower frequencies are going to be replicated or aliased in the higher frequency buckets. So the frequencies will start from 0, increase by 172 Hz for each coefficient up to the N/2 coefficient, then decrease by 172 Hz until the N - 1 coefficient.

That should be enough information to get you started. If you would like a much more approachable introduction to FFTs than is given on Wikipedia, you could try Understanding Digital Signal Processing: 2nd Ed.. It was very helpful for me.

So that is what those numbers represent. Converting to a percentage of height could be done by scaling each frequency component magnitude by the sum of all component magnitudes. Although, that would only give you a representation of the relative frequency distribution, and not the actual power for each frequency. You could try scaling by the maximum magnitude possible for a frequency component, but I'm not sure that that would display very well. The quickest way to find a workable scaling factor would be to experiment on loud and soft audio signals to find the right setting.

Finally, you should be averaging the two channels together if you want to show the frequency content of the entire audio signal as a whole. You are mixing the stereo audio into mono audio and showing the combined frequencies. If you want two separate displays for right and left frequencies, then you will need to perform the Fourier Transform on each channel separately.

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