Hello Alessandro,
On 10/12/2012 12:18 PM, Alessandro Saccoia wrote:
> In my original question, I was thinkin of mixing signals of arbitrary
> sizes.
I don't think you have been clear about what you are trying to achieve.
Are you trying to compute the sum of many signals for each time point?
Or are you trying to compute the running sum of a single signal over
many time points?
What are the signals? are they of nominally equal amplitude?
Your original formula looks like you are looking for a recursive
solution to a normalized running sum of a single signal over many time
points.
I could relax this requirement, and forcing all the signals to
be of a given size, but I can't see how a sample by sample summation,
where there are M sums (M the forced length of the signals) could
profit from a running compensation.
It doesn't really matter whether the sum is accross samples of a signal
signal or accross signals, you can always use error compensation when
computing the sum. It's just a way of increasing the precision of an
accumulator.
Also, with a non linear
operation, I fear of introducing discontinuities that could sound
even worse than the white noise I expect using the simple approach.
Using floating point is a non-linear operation. Your simple approach
also has quite some nonlinearity (accumulated error due to recursive
division and re-rounding at each step).
Ross
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