Conversion of linear FFT into *more* log-spaced frequency bins?

Whenever you convert a linear frequency axis to a log one, there will be a “hinge point”. Above that hinge point you are throwing away information, below that hinge point you keep all information and the representation becomes redundant.

So it’s less about whether the total number of point is more or less, but weather you operate in the “information-loss” or “redundant” region or both. Inherently, there is nothing wrong with redundancy and there are good use cases for it. If this makes live for your CNN easier, by all means go for it. You just have to be aware, that the larger size of the data does NOT mean more independent information.

Theoretically: are there any established techniques designed for the output described here (e.g. converting N-sized FFT frame to >N sized log-spaced representation)?

Increasing the apparent frequency resolution at low frequencies is fairly common in audio. The easiest way is by interpolation in the redundant region. Zero padding in the time domain is most commonly used for that, but there are other interpolation methods with different complexity/quality trade-offs.

Another common alternative is be to use parallel STFT chains where the redundant (lower frequency) regions have longer time windows and more overlap. That’s partially motivated by the fact that lower frequencies can’t change as fast as higher ones. So the redundancy here is smeared out over time.

Practically: Is my intuition correct that for signal analysis, this conversion only makes practical sense with fewer log-spaced bins?

No. It’s certainly common practice to add redundant low frequency data in audio, even just for “cosmetics” to make the low end look smoother on a graph.