The real magic in generative modeling comes from the post training process that comes after, which usually (e.g., RLHF) approximates Reverse KL (given limited capacity, try to perfectly cover what you can, but it's fine to drop the rest entirely). This gives amazing results, but is also the cause of AI oddities like the "AI Image Pixar Look", many of the verbal tics of LLMs, and all AI music using the same small set of voices. Jensen-Shannon Divergence sits right in the middle of Forward and Reverse KL and is what many GANs are claimed to approximate. Ideally, it is a better trade-off between diversity and fidelity.

This isn't like text classification, the signal many orders of magnitude higher bitrate and so many more corners need to be cut. It's likely going to be nearly impossible or at least not remotely worth it to generate an audio signal that is truly undetectable in the foreseeable future.