Well, since 1/20µs = 50 kHz, it strikes me that this threshold was probably chosen with audio in mind.
I agree that for a lot of cases, it's plenty. But if you're trying to account for phase differences in multiple data sources, this puts a major blindfold on.
Let's think about it in terms of lower frequencies. Imagine that you're sampling from a pool of events that occur roughly 100 times/hour. Let's say you want to sample 2 event per hour.
Well you might say "since you're only sampling at a rate of 2/hr, why would you need any better granularity than 30 minutes?"
Here's the catch: you need to get the event that's closest to the 15 minute mark of each hour and the one that's closest to the 30 minute mark; you will then put those two together using your special formula.
Imagine that all your events came into a mailbox on an half-hourly basis. Imagine a stack of ~100 envelopes, each labeled "8 - 8:30 A.M." or "8:30 - 9 A.M.". You're tasked tasked with the job of trying to pick the two closest to 8:15 A.M. and 8:30 A.M. Good luck! It's gonna be hard, if not impossible, to do this consistently with any accuracy.
(Even if you had 15 minute granularity, you'd still have to pick from each one from either 25 or 50 envelopes, depending on where the windows fall relative to the 15 minute marks.)
Now, to make matters even more complicated, imagine a situation where you're integrating each hour's result with the previous hours' result, e.g. taking a rolling sum or product, or computing some kind of feedback/delay/reverb filter. In cases like that, you're done for.
Full disclosure, I'm coming at this from the perspective of an experimental digital artist who some day would like to do interesting creative things with high frequency sources, like the sounds of bugs, dolphins, and higher frequency ambient sounds. I'm also interested in building applications that crowd-source and integrate audio from multiple smart-phones in a room. The web seemed like a perfect platform for this.
I agree that for a lot of cases, it's plenty. But if you're trying to account for phase differences in multiple data sources, this puts a major blindfold on.
Let's think about it in terms of lower frequencies. Imagine that you're sampling from a pool of events that occur roughly 100 times/hour. Let's say you want to sample 2 event per hour.
Well you might say "since you're only sampling at a rate of 2/hr, why would you need any better granularity than 30 minutes?"
Here's the catch: you need to get the event that's closest to the 15 minute mark of each hour and the one that's closest to the 30 minute mark; you will then put those two together using your special formula.
Imagine that all your events came into a mailbox on an half-hourly basis. Imagine a stack of ~100 envelopes, each labeled "8 - 8:30 A.M." or "8:30 - 9 A.M.". You're tasked tasked with the job of trying to pick the two closest to 8:15 A.M. and 8:30 A.M. Good luck! It's gonna be hard, if not impossible, to do this consistently with any accuracy.
(Even if you had 15 minute granularity, you'd still have to pick from each one from either 25 or 50 envelopes, depending on where the windows fall relative to the 15 minute marks.)
Now, to make matters even more complicated, imagine a situation where you're integrating each hour's result with the previous hours' result, e.g. taking a rolling sum or product, or computing some kind of feedback/delay/reverb filter. In cases like that, you're done for.
Full disclosure, I'm coming at this from the perspective of an experimental digital artist who some day would like to do interesting creative things with high frequency sources, like the sounds of bugs, dolphins, and higher frequency ambient sounds. I'm also interested in building applications that crowd-source and integrate audio from multiple smart-phones in a room. The web seemed like a perfect platform for this.