Absolute pitch is the ability to correctly identify any musical note. It is close to another ability, relative pitch, which is the ability to identify any interval correctly, although relative pitch is usually described as the ability to correctly identify any note, once the subject has been given a "reference tone". An important fact here is that relative pitch is not as rare as absolute pitch, which may hint that absolute pitch is harder to acquire/train.
What kind of learning procedure would you design to learn absolute pitch?
In particular, I have two strategies in mind, and I don't think either would work. The first one is simple: a computer produces a note, the user identifies the note, the computer corrects the user. This is flawed because the first note in pair with the correct answer given by the computer provides a reference tone, therefore, after the first note, the user only trains his/her relative pitch. The second strategy would be to not correct the user immediately, but instead wait for example 10 notes before correcting. This seems flawed too, because of how crucial a quick feedback is to learning. Note that, with either strategies, it is not possible to make long learning sessions anyway, because it soon becomes a relative pitch training strategy.
Any clever idea?
I gave this an upvote because it is directly counter to my current belief about how relative/absolute pitch work and interact with each other. I agree that if someone's internalised absolute pitch can constantly identify out of tune notes, even after minutes of repetition, this is a strong argument against my position. On the other hand, maybe they do produce one internal reference note of set frequency, and when comparing known intervals against this, it returns "out of tune" every time. I can see either story being true, but I would like to hunt down some more information on which of these models is more accurate