Manage co-occurence of events about scaper HOT 2 OPEN

Cheers @turpaultn !

We could definitely add support for non-uniform discrete sampling, e.g. via a new choose_weighted distribution tuple.

IIUC in the example above you're providing the probability for each event being chosen, and then choosing one of these events, but that's not the same as co-occurrence probabilities, right? That is, it's different to say

1. Choose between alarm/dog with prob .7/.3
2. Give me a soundscape where alarm and dog co-occur with probability X.

My understanding from today's meeting was that the team is interested in the latter, but maybe I misunderstood?

Regardless, it looks like we'd need something like choose_weighted to support Gibbs or related types of sampling methods?

from scaper.

Cool !

Well, I understand it's not clear, because I've put this little piece of code.

But the algorithm is like this:

• Sample an event from the 10 classes (uniformly)
• Take the dictionnary of co-occurence above (what's the probability of alarm/dog when "cat" is the first event)
• Sample an event using this dictionnary (for example, no chance a vacuum_cleaner would be picked, there is only dog and alarm left)

The idea was that if an alarm ("bip") appeared for example, there is a lot of chance you can hear another one.
As I said, it is simple, but at least we were able to have a class balance closer to the real set without going spending too much time.

Regardless, it looks like we'd need something like choose_weighted to support Gibbs or related types of sampling methods?

I agree.

from scaper.