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Is there any plausible method to estimate or predict one signal, on the basis of known value of another signal, provided the two signals bear a strong correlation?

For instance, the audio amplitude and the moving pixels in a video, where the objects responsible for sound are in motion, are correlated usually. So, can the correlation between the two be exploited in any manner to predict the audio amplitude, if it goes missing, from the moving pixels?

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  • $\begingroup$ Hi: if the correlation is liner, then this means that you can back out the coefficient of a simple univariate regression model for optimal prediction. I forget the formula. I think it's beta coefficient = cov(x,y)/var(x). $\endgroup$ – mark leeds Jan 10 at 15:25
  • $\begingroup$ The short answer is "yes" and "Regression". The long answer involves techniques for "locking on" to those video elements that will return some sequence of $(x,y)$ points out of which you will derive the quantity you are trying to regress with the amplitude. Do you think you could talk a little bit more about the problem? $\endgroup$ – A_A Jan 10 at 15:26
  • $\begingroup$ Sure. I am looking to work with videos where the source of sound is the moving pixels in the video. So, basically, greater the motion, larger the sound - like a person running. With that, I want to find the pixels in the video frames that are responsible for the sound, and use the correlation between the audio and the pixels somehow to predict any missing audio for a particular frame, if the case may arise that the audio gets missing. $\endgroup$ – Curiosity Jan 10 at 15:55

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