Should I pass Kalman Filter absolute or offset-from-mean sensor values?
As mentioned in another answer, the variance of the measurement noise is a property of the model which is baked in to the Kalman filter.
I think you're running into a different problem, which is that your model is different from reality.  It sounds like you're modeling the system assuming that all the microphones are 'live'.  If that's the case, then the filter won't take a dead microphone as something to be ignored -- it'll take a dead microphone as a live microphone that's 'hearing' utterly valid silence.
Note that Kalman filters can do some apparently absurd things in the presence of a model mismatch.  Because they assume Gaussian distributions for everything, and because the PDF of a Gaussian drops off so profoundly as you stray from the mean, they really don't do a good job handling real-world processes that have low kurtosis, nor do they handle model mismatch very well.
You need to modify your filter, or sanitize its input.  It's probably best to modify the filter by augmenting it with an external &quot;dead microphone&quot; detector (and make sure you get it right -- you're basically automatically throwing out outliers, and there's known problems with doing that too enthusiastically).  Or, make sure a human goes through the data and makes sure the microphones are live.