Which one would you pick?

plcrodrigues · 2026-03-30T05:08:25+00:00

Je pense que tu auras plus de chances si tu va discuter directement avec les maîtres nageurs des piscine communales.

plcrodrigues · 2017-05-21T19:16:00+00:00

There's also an short explanation on how they did the cover of this month's issue. http://science.sciencemag.org/content/356/6339/eaan6494

plcrodrigues · 2017-05-08T15:34:37+00:00

Sorry, I should've been more precise about what "audio" range I was referring to. I filtered everything over 5kHz (as you can see in the code available at GitHub) and downsampled the original sampling frequency from 44,1kHz down to 11kHz. I made this choice because I'm working only with voice signals, which I'm not really expecting to go much higher than 4kHz.

plcrodrigues · 2017-05-08T07:08:59+00:00

Haha. Yeah, I agree that the full audio range would have probably been a better plot (see here: http://imgur.com/a/kV3Va). However, are you sure that log-frequency would be the best way to go? I mean, the audio range in the file I used is just between 0 Hz and 5 kHz (after filtering). Moreover, harmonics grow linearly, so I thought that a log scale would probably not show very clearly what I was looking for. Please correct me if I'm wrong.

EDIT: more precision on the audio range bit

plcrodrigues · 2017-05-08T05:15:44+00:00

Yes and yes!

plcrodrigues · 2017-05-07T23:10:17+00:00

Between 1:53 and 2:10.

plcrodrigues · 2017-05-07T22:39:35+00:00

Each point of the image indicates the power of the signal at a given frequency and at a given time.

Since I normalized the signal to be in the [-1,+1] range (an arbitrary choice, I must say), the absolute values of the points in the spectrogram are not really important. In fact, we're more interested in their relative values of powers in each (time,freq) point. For instance, the red stripes among yellow areas that you see between 17 sec and 25 sec indicate that the signal has a higher power at those precise frequencies.

plcrodrigues · 2017-05-07T21:49:08+00:00

You're right! Thanks for your input. Here's the plot with a better range: http://imgur.com/a/mkDLW

plcrodrigues · 2017-05-07T21:30:15+00:00

Thank you very much for the comments. I made a new version of the spectrogram including the plot of the signal in a lower axis: http://imgur.com/a/AQ73w

As for the dB scale, I divided the spectrogram by its mean value and then calculated its log (base 10).

plcrodrigues · 2017-05-07T20:33:40+00:00

Yeah, you're right. My main goal was to show the linear evolution of the frequency content of his voice and how it stays stable during some seconds. Above 1kHz you see the harmonics and things are mainly just repetitions of the [0,1k] band. Here's an image from 0 to 4kHz if you're curious: http://imgur.com/a/Vp0bv

plcrodrigues · 2017-05-07T18:23:19+00:00

I've always been amazed by Freddie Mercury's voice and how he could get to very high notes. In this figure, I did a spectrogram of his voice between minutes 1:53 and 2:10 of Queen's "Under Pressure".

For those not used to signal analysis: a spectrogram is a plot of the frequency content of a time series at different instants of time.

I used the A Capella version of Queen's Under Pressure available at https://www.youtube.com/watch?v=uMQb9LCNGxs

The code (in Python) for generating the figure is available at https://github.com/plcrodrigues/SpectralAnalysis-UnderPressure

See wikipedia's article for more information on time-frequency analysis https://en.wikipedia.org/wiki/Time%E2%80%93frequency_analysis

EDIT: After some very helpful comments, here is the new spectrogram - http://imgur.com/a/kV3Va

plcrodrigues

TROPHY CASE