Filler Sound Detection v0.8
complete
Jacob Bozarth
We're consistently developing new versions of our Filler Sound Detection machine learning model to reduce false positives (words that sound like um, but are not), improve the precision of boundaries (start and end times), and increase the number of mistakes it can accurately identify.
Colby
complete
After 4 years of research and training, Filler Sound Detection V1 is here!
This new machine learning model has reached nearly a 90% acceptance rate among test users (the percent of edits users accept).
Translation? Filler Sound Detection is insanely accurate.
You can expect less false alerts, more precise boundaries, and the model is smart enough to even leave in some ums that it anticipates you'll want to keep (gotta stay authentic, right?).
90% is extremely high, but this doesn’t tell the whole story.
We’ve reached a level so accurate that even pro audio engineers cannot consistently agree on how to properly edit the last 10% of edits.
Next, we’ll add a feature that cuts all the accurate edits automatically so you just have to review the harder 10%. It will be MUCH faster.
See all updates at resound.fm/release-notes!
Colby
in progress
Colby
planned