Spectrogram output versus figure

When using spectrogram to calculate power spectral density for a large dataset, the plot produced by directly running the command with no output arguments is slightly different from what I get when I run it with an output argument and plot that output myself.

For example, the following plots spectrograms for a subset of my data and compares the direct result of spectrogram with the output raster:
load(‘exampledata.mat’)
window = 10000;
noverlap = round(0.1*window);
fs = 10000;

figure(1);clf
t = tiledlayout(3,1, ‘TileIndexing’, ‘columnmajor’);

% Plot spectrogram directly from spectrogram function
s(1) = nexttile;
spectrogram(exampledata,window,noverlap,window,fs);
title(‘Original spectrogram plot (real data)’)
PSDdB_orig = get(get(gca,’Children’),’CData’);
caxis([min(PSDdB_orig,[],’all’) max(PSDdB_orig,[],’all’)])

% Calculate power spectral density and plot as decibels
[~,F,T,PSD] = spectrogram(exampledata,window,noverlap,window,fs);
PSDdB = (10.*log10(PSD))’;

s(2) = nexttile;
imagesc(F./1000,T./60,PSDdB)
title(‘Spectrogram output raster (real data)’)
c = colorbar;
caxis([min(PSDdB,[],’all’) max(PSDdB,[],’all’)])
set(get(c,’label’),’string’,’Power/frequency (dB/Hz)’,’Rotation’,90);

% Calculate ratio of the two approaches above and plot that
PSDdiff = PSDdB_orig./PSDdB;

s(3) = nexttile;
imagesc(F./1000,T./60,PSDdiff)
title(‘Original plot data divided by output raster data’)
c = colorbar;
caxis([min(PSDdiff,[],’all’) max(PSDdiff,[],’all’)])
set(get(c,’label’),’string’,’original/output’,’Rotation’,90);
set(s(2:3),’YDir’,’normal’)
xlabel(s(2:3), s(1).XLabel.String)
ylabel(s(2:3), s(1).YLabel.String)

From the figures above, it looks like they produce nearly the same result, except at high frequencies (the middle figure looks more different than the first mostly due to the different color scales).
Comparing the ranges and plotting a cross-section (example spectra at a single time) from each one shows that the difference seems to be that spectrogram’s direct plotting method is cutting off local minima:
disp([‘min/max of original spectrogram plot: ‘,num2str(min(PSDdB_orig,[],’all’)),’, ‘,num2str(max(PSDdB_orig,[],’all’))])
disp([‘min/max of spectrogram output raster: ‘,num2str(min(PSDdB,[],’all’)),’, ‘,num2str(max(PSDdB,[],’all’))])
% Plot example spectra from each approach
figure(2);clf
subplot(2,1,1);
plot(F./1000,PSDdB(10,:),F./1000,PSDdB_orig(10,:),’–‘);
legend(‘Spectrogram output raster’,’Original spectrogram’)
title(‘Example spectra’)
xlabel(‘Frequency (kHz)’)
ylabel(‘Power/frequency (dB/Hz)’)

% zoom in on high frequency range where values differ
subplot(2,1,2,copyobj(gca,gcf))
xlim([4.75 4.85])
title(‘high frequency close-up’)

But weirdly, I can’t reproduce this with randomly generated synthetic data:
x = randn(length(exampledata),1);

figure(3);clf

t = tiledlayout(3,1, ‘TileIndexing’, ‘columnmajor’);

s(1) = nexttile;
spectrogram(x,window,noverlap,window,fs);
title(‘Original spectrogram plot (synthetic data)’)
PSDdB_orig = get(get(gca,’Children’),’CData’);
caxis([min(PSDdB_orig,[],’all’) max(PSDdB_orig,[],’all’)])

[~,F,T,PSD] = spectrogram(x,window,noverlap,window,fs);
PSDdB = (10.*log10(PSD))’;
PSDdiff = PSDdB_orig./PSDdB;

s(2) = nexttile;
imagesc(F./1000,T./60,PSDdB)
title(‘Spectrogram output raster (synthetic data)’)
c = colorbar;
caxis([min(PSDdB,[],’all’) max(PSDdB,[],’all’)])
set(get(c,’label’),’string’,’Power/frequency (dB/Hz)’,’Rotation’,90);

set(s(2:3),’YDir’,’normal’)
xlabel(s(2:3), s(1).XLabel.String)
ylabel(s(2:3), s(1).YLabel.String)
disp([‘min/max of original spectrogram plot: ‘,num2str(min(PSDdB_orig,[],’all’)),’, ‘,num2str(max(PSDdB_orig,[],’all’))])
disp([‘min/max of spectrogram output raster: ‘,num2str(min(PSDdB,[],’all’)),’, ‘,num2str(max(PSDdB,[],’all’))])

I do notice that the minimum PSD value for my data (-196 dB) is significantly lower than the minimum of the synthetic data. Is there a lower limit where spectrogram starts to censor spectra (say, around -156.5351 dB)? Or is something else going on here? Any help would be much appreciated!! Thanks!When using spectrogram to calculate power spectral density for a large dataset, the plot produced by directly running the command with no output arguments is slightly different from what I get when I run it with an output argument and plot that output myself.

figure(1);clf
t = tiledlayout(3,1, ‘TileIndexing’, ‘columnmajor’);

% Calculate power spectral density and plot as decibels
[~,F,T,PSD] = spectrogram(exampledata,window,noverlap,window,fs);
PSDdB = (10.*log10(PSD))’;

% Calculate ratio of the two approaches above and plot that
PSDdiff = PSDdB_orig./PSDdB;

% zoom in on high frequency range where values differ
subplot(2,1,2,copyobj(gca,gcf))
xlim([4.75 4.85])
title(‘high frequency close-up’)

But weirdly, I can’t reproduce this with randomly generated synthetic data:
x = randn(length(exampledata),1);

figure(3);clf

t = tiledlayout(3,1, ‘TileIndexing’, ‘columnmajor’);

[~,F,T,PSD] = spectrogram(x,window,noverlap,window,fs);
PSDdB = (10.*log10(PSD))’;
PSDdiff = PSDdB_orig./PSDdB;

I do notice that the minimum PSD value for my data (-196 dB) is significantly lower than the minimum of the synthetic data. Is there a lower limit where spectrogram starts to censor spectra (say, around -156.5351 dB)? Or is something else going on here? Any help would be much appreciated!! Thanks! When using spectrogram to calculate power spectral density for a large dataset, the plot produced by directly running the command with no output arguments is slightly different from what I get when I run it with an output argument and plot that output myself.

figure(1);clf
t = tiledlayout(3,1, ‘TileIndexing’, ‘columnmajor’);

% Calculate power spectral density and plot as decibels
[~,F,T,PSD] = spectrogram(exampledata,window,noverlap,window,fs);
PSDdB = (10.*log10(PSD))’;

% Calculate ratio of the two approaches above and plot that
PSDdiff = PSDdB_orig./PSDdB;

% zoom in on high frequency range where values differ
subplot(2,1,2,copyobj(gca,gcf))
xlim([4.75 4.85])
title(‘high frequency close-up’)

But weirdly, I can’t reproduce this with randomly generated synthetic data:
x = randn(length(exampledata),1);

figure(3);clf

t = tiledlayout(3,1, ‘TileIndexing’, ‘columnmajor’);

[~,F,T,PSD] = spectrogram(x,window,noverlap,window,fs);
PSDdB = (10.*log10(PSD))’;
PSDdiff = PSDdB_orig./PSDdB;

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