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On the internet, I have seen people use Matplotlib/Python and short-time fourier transformations to create spectrograms. Are their other formula/algorithms to create spectrograms? And are they possible to implement in python or be called through command prompt?

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  • $\begingroup$ i have MATLAB code that opens an audio file and creates a spectrogram using surf(). is that what you're looking for? it's pretty simple. $\endgroup$ – robert bristow-johnson Jun 21 at 3:52
  • $\begingroup$ @robertbristow-johnson I was asking about a command line called exe/program that can use different algorithm/formulas to create a spectrogram. I notice that they all seem to use short-time Fourier transformations, and I was wondering if there were different formulas. $\endgroup$ – itisyeetimetoday Jun 21 at 4:32
  • $\begingroup$ there all pretty much the same formula. different windows, different frame widths and different frame hop distances. $\endgroup$ – robert bristow-johnson Jun 21 at 5:01
  • $\begingroup$ @LaurentDuval No, but thanks for the advice. My final project was too similar compared to Melnet, using spectrogram for speech synthesis, to be worth pursuing for ISEF. $\endgroup$ – itisyeetimetoday Jul 29 at 4:16
  • $\begingroup$ Do not hesitate then to upvote answers and validate the most useful one $\endgroup$ – Laurent Duval Jul 29 at 20:43
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so here's a short MATLAB script that will compute and draw a spectrogram.

if ~exist('inputFile', 'var')
    inputFile = 'tom_hit.wav';
end

if ~exist('frameLength', 'var')
    frameLength = 8192;
end
frameLength = 2.^(ceil(log2(frameLength) - 1e-10));

if ~exist('frameHop', 'var')
    frameHop = 256;
end
frameHop = 2.^(floor(log2(frameHop) + 1e-10));
frameHop = min(frameHop, frameLength/2);                                    % insure at least 50% overlap


[inputBuffer, Fs] = audioread(inputFile);

[inputFileSize, nChannels] = size(inputBuffer);

nSamples = 2.^(ceil(log2(inputFileSize + frameLength) - 1e-10));            % zero-pad on both sides and round up to nearest power of 2

nFrames = floor((nSamples-frameLength)/frameHop);

x = zeros(nSamples, 1);
x(frameLength/2+1:frameLength/2+inputFileSize) = inputBuffer(:,1);          % use left channel only, zero-pad half frame on both sides

clear inputBuffer;                                                          % free this memory

dB_floor = -90;

beta = 5;
frameWindow = gausswin(frameLength, beta);                                  % gaussian window

XX = zeros(nFrames, frameLength/2+1);

for frame = 1:nFrames

    X = fft( fftshift( x((frame-1)*frameHop+1:(frame-1)*frameHop+frameLength, 1).*frameWindow ) );

    XX(frame,:) = abs( X(1:frameLength/2+1) ).^2 + 10^(dB_floor/10);        % leave XX as energy with dB_floor dB floor.

end

figure(1);
[taxis, faxis] = meshgrid(0:Fs/frameLength:Fs/2, 0:frameHop/Fs:(nFrames-1)*frameHop/Fs);
surf(faxis, taxis, 10*log10(XX), 'EdgeColor','none', 'LineStyle','none');
xlabel('sec')
ylabel('Hz')
zlabel('dB')
view(40,40);
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Sometimes people use time-frequency representations (TFRs) as a generalization of the spectrogram.

This C code calculates some different TFRs of Cohen’s class: bilinear TFRs.

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There are spectral analyzers that come with executables (and sometimes source code) like:

Even FFMpeg can do the job: ffmpeg -i inputfile.mp3 -lavfi showspectrumpic=s=800x400:mode=separate spectrogram.png

The basic formula is typically composed of:

  • select a frame, and apply a window,
  • compute a Fourier transform,
  • display magnitude or square magnitude values.

as detailed by Robert. As Peter wrote, there are many extensions or reformulation, termed time-frequency distributions or time-scale representations, that can be implemented as filter banks, quadratic formulae, etc. Among script language implementations, you can check:

This is still an open research topic.

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