Determine speed of roulette ball through audio analysis?

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Discussion Overview

The discussion revolves around the feasibility of tracking the speed of a spinning roulette ball using audio analysis techniques. Participants explore various methods for analyzing audio data to extract meaningful information about the ball's speed, considering both theoretical and practical challenges.

Discussion Character

  • Exploratory
  • Technical explanation
  • Debate/contested
  • Mathematical reasoning

Main Points Raised

  • One participant suggests using a real-time spectrum analyzer but notes difficulties in identifying dominant frequencies due to background noise.
  • Another participant proposes looking for repetitive spikes or frequencies in the audio data corresponding to the ball's motion.
  • A suggestion is made to use autocorrelation of the time waveform to identify intervals between 'identical' clicks, with a caution about the need to window the waveform due to the ball's deceleration.
  • One participant introduces the idea of using the Doppler effect to detect pitch changes as the ball moves towards and away from the microphone, potentially providing a decay function for speed calculation.
  • Another participant expresses uncertainty about implementing the Doppler effect amidst background noise and suggests using large sample sizes for analysis, while also considering the need for trial and error in processing.
  • Several participants share spectrograms of the audio data, noting a lack of clear patterns and discussing the frequency ranges that may be relevant to the ball's speed.
  • Concerns are raised about the limitations of audio analysis, including the impact of lossy compression on the sound data and the challenge of determining the actual speed of the roulette wheel itself.

Areas of Agreement / Disagreement

Participants express a range of ideas and hypotheses, but there is no consensus on a specific method or solution. Multiple competing views and uncertainties remain regarding the effectiveness of the proposed techniques.

Contextual Notes

Participants note limitations such as the dependence on the quality of audio data, the effects of compression artifacts, and the challenge of distinguishing relevant frequency ranges amidst background noise.

fwbfwb
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I'm trying to find a way to track the speed of a spinning roulette ball in real time through audio analysis, i.e. have a computer report the speed of the spinning ball it hears through a microphone.Here is a video of a spinning ball:

I've tried watching a real time spectrum analyser (FFT), but haven't found any dominant frequencies to focus on, and there was a lot of noise. It might be possible if I find a very specific range of bins to track but I haven't found any yet.

Here is a snapshot of the frequency domain 20 seconds in. It doesn't change much except get slightly quieter overall as the spin goes on.
f4cnkiB.png


Here is what the audio of one spin looks like in the time domain. My brain can see the trend of the ball's speed slowing, but tracking amplitude alone would not work with background noise like music or talking.
kHx2CYr.png
Any ideas? My brain can perceive the ball slowing when I listen so it must be somehow possible to get a computer to recognize it.
 
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@fwbfwb
Hi. Great video of the ball rolling around the roulette wheel.
I must say, though, finally a rival has arrived to challange the champion "Watching Paint Dry".:rolleyes:

Seriously though, a good question.
Unfortunately, I cannot think of anything at the moment to add to help you your analysis.

except maybe to ask if,
You haven't found any occurrence of repetative spikes, frequencies, etc on each revolution?
 
My first reaction to this is that you could try autocorrelation of the time waveform. That would have the effect of finding the time interval between repeats of 'identical' clicks. Because the ball is slowing down, it may be wise to window the waveform.
It could be worth while calibrating your system with an optical measurement.
Is this a new idea for a Casino Heist?
 
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what about using the Doppler effect?
If the microphone is not directly above or below the table you should be able to detect a rhythmic rise and fall in the pitch as the ball moves toward, then away from the microphone. That would at least give you the duration of each revolution, which should give you a rate of decay function that you can use to calculate the speed of the ball at any given point on any given orbit.
 
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mrspeedybob said:
what about using the Doppler effect?
I'm not sure how you could implement that, in the presence of all the shash in the time and frequency domain data. `There will be a number of possible approaches but they would be better if they could use large numbers of samples in order to get the information out of all those apparently random values. Low pass filtering of the frequency data would be throwing away a lot of potentially useful data. Autocorrelation would look for repeats in time. But even then, as the speed is decreasing, it could be better to do something really smart and change the time steps, used in the integration to take the acceleration into account. That would require some idea of the acceleration involved - or perhaps, just assume linear acceleration and then sweep through, looking (and identifying) when the resulting waveform 'looks' best. A lot of trial and error processing, I guess but there are many packages that will do it at the push of a button.
 
For what it's worth here is a spectrogram of the first spin in the video.
upload_2015-12-13_16-12-51.png

I can't make out any useful change of pattern.

(Generated using the freeware Audacity.)
upload_2015-12-13_16-12-51.png
 
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DrGreg said:
For what it's worth here is a spectrogram of the first spin in the video.

I can't make out any useful change of pattern.

(Generated using the freeware Audacity.)View attachment 93313
The frequency scale on the attachment seems to have a lower limit of 300Hz (?- there are no labels on the axes so I may be making assumptions). The frequency range, relating to the speed of the ball would be around 10Hz (the rate it hits the sides of the dents on the wheel. Can you change the frequency range on the software to include the appropriate frequency?
Of course, there is the other issue of finding the actual speed of rotation of the wheel . . . . Analysis of the sound will only give the relative speeds.
 
sophiecentaur said:
The frequency scale on the attachment seems to have a lower limit of 300Hz (?- there are no labels on the axes so I may be making assumptions). The frequency range, relating to the speed of the ball would be around 10Hz (the rate it hits the sides of the dents on the wheel. Can you change the frequency range on the software to include the appropriate frequency?
Of course, there is the other issue of finding the actual speed of rotation of the wheel . . . . Analysis of the sound will only give the relative speeds.
Sorry, I should have said the vertical scale is Hz and the horizontal scale is seconds.

Here's one which goes down 1 Hz, with a 16384-point Hanning window. (I tried other window sizes and types, but none seemed to reveal more detail. It's difficult to find the right trade-off between time-resolution and frequency-resolution.) The sampling rate is 48 kHz.

Is it my imagination, or is there something dropping from about 7 Hz to 4 Hz and then vanishing at 32 s?

We must bear in mind the sound has come from a youtube video and has therefore been subject to lossy compression. Some of the mush may be compression artefacts.

upload_2015-12-13_17-18-14.png


upload_2015-12-13_17-18-14.png
 
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I think you could be right about the low frequency bits. There seem to be three definite horizontals (5,7,and 9 Hz ish). I wonder what the three of them mean. Plus there are a number of horizontals at higher frequencies. Your guess is as good as mine.
The "lossy compression" will certainly have done its bit to spoil those lf components. You don't hear them so compression won't look after them and they could even be non linear products.
It would be interesting to see what the same software makes of the original data.
 
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