Comment 3 for bug 1882776

The interquartile mean effectively takes into account only half of the data points you give to it. From this point of view and assuming a data set with no outliers, interquartile mean needs twice as much data points to get the same accuracy than the regular mean. But with enough data points, it is just as accurate.

The regular mean is very sensitive to outliers. In this case, if you miss one single tap, you almost surely get the bpm wrong. Even if you have bad timing in one or two taps. This is the problem that interquartile mean solves.

If increasing its lenght alone doesn’t improve the behaviour of the filter, I’d try to drop fewer data points, i.e. generalizing the current interquartile mean to a truncated mean where we can choose a discard threshold other than the quartile.