Open alecristia opened 3 years ago
I think we should consider quality flags for recordings. In this case, the flag would be might_feature_gaps
and the recordings for which this flag is set to 1 would be discarded when relevant.
In this case, and more generally (especially when we don't have redundant recordings), we could flags all audios that have undergone merges. But before doing so I would like to hear from the authors about the following questions :
In any case, I am keeping the list of audios for which I had to perform merges. If you look at the commented bits of code in the notebook, you'll see I used this selection criterion to filter out mismatching pairs initially.
I can provide partial replies to this, and I don't think it'll be useful to ask about all of these to the SolIs team - so apologies for not forwarding all Qs.
RE: why were some audios originally split:
RE the descriptives, let's talk about it on Tue!
one more thought:
Imagine a file gets split, and you're a tired RA, who's adding the prefixes to the files. You could make a mistake:
Can you think of ways of telling these mistakes apart?
I also thought that mistakes are probably 'grouped' (in that a tired person makes more mistakes). So another way to flag files as problematic is based on the typos we saw in the naming. Did you keep a record of that, and would that help us choose?
one more thought on that: if I mix up the order within a recording, VTC should still return a similar quantity of speech -- but not if I mix across kids
These Qs refer to this version of Voice type classifier stability.pdf
Data inclusion
This has changed from a previous version, in which files that had been 'glued together' were considered suspect (right), but we end up with 50 pairs removed in both cases. So these are the same files? (ie does desyncing & gluing together lead to the same pairs raising alarm?)
Also, we wouldn't want to lose the 50 babies, so how can we decide which USB is more trustworthy? Can we simply use the file with longest duration? Right now, we assigned rec 1 & 2 to the two recs randomly, but perhaps the patterns will be clearer if rec 1 is the "primary" (longer duration) and rec 2 is the "secondary" (shorter duration and/or glued together).
If that option doesn't feel right, a more complicated thing we can try is to use the shift patterns to determine which rec is more trustworthy, trying to exploit the fact that if a rec is more complete, then it will have sections that the other rec doesn't have.
(My main aim here is to have data from as many children as possible, and to minimize inclusion of messy data.)
Details on descriptives (section 5)