Comment by sublinear

> the synthesis was marked by a classification model as more duck than the real duck sample

The quality of a recording can throw off most classifiers.

Models we have today are so dependent on the quality of training data, that it's questionable how we will sustain long term what we are currently calling "AI".

i.e. What happens when the cameras and microphones everyone has in their pockets are significantly higher quality and the AI model hasn't had a total refresh in a long time? How does that cause classification to drift enough to fail?