Let’s say I have an FFT output that’s many, many, bins wide, and I want

to compress that information into a narrower

display (let’s say from 4M bins down to 1024 bins).

My approach has been to sum up each set of [4M/1024] bins, and use that

as the final output. But should I be averaging

across the bins? That is, should I be taking each 3906bin group from

the 4M bin wide spectrum and computing an average

across those bins, and stuffing it into the appropriate place in the

1024-wide spectrum, or something else?

Seems to me that if I have 4000 1Hz-wide bins, I should sum them to give

me the total power in a single bin that

“represents” the same amount of bandwidth. But is it more subtle than

that?

–

Marcus L.

Principal Investigator, Shirleys Bay Radio Astronomy Consortium

On Sun, Mar 8, 2009 at 6:13 PM, Marcus D. Leech [email protected]

wrote:

Seems to me that if I have 4000 1Hz-wide bins, I should sum them to give

me the total power in a single bin that

“represents” the same amount of bandwidth. But is it more subtle than

that?

As usual, yes and no.

If you’re concerned with statistical hygiene, then the mean (averaging

over

multiple bins) is defensible. And if you wanted to add a dimension to

each

reduced output bin, like color, you might want to throw in the variance

within each set of sub-bins contributing to the average.

The most robust estimator would be the median, though, probably – the

exact

midpoint between the lowest and highest values in each set of sub-bins.

However it sounds like what you’re going for is a kind of compression

that’s

lossy but optimizes for visual properties, not statistical robustness.

That’s usually highly non-linear and very subjective. In that case, why

not

pick what just looks good on your data? The algorithm that John

Ackermann

suggests is likely to be as good as anything else.

Frank