The direct counter-argument to "worst representation" is usually "representation with fewest assumptions", waveform as shown here is getting close. Though recording environment, equipment, how the sound actually gets digitized, etc. also come into play, there are relatively few assumptions in the "waveform" setup described here.
I would say in the neural network literature at large, and in audio modeling particularly, this continual back and forth of pushing DSP-based knowledge into neural nets, on the architecture side or data side, versus going "raw-er" to force models to learn their own versions of DSP-style transforms has been and will continue to be a see-saw, as we try to find what works best, driven by performance on benchmarks with certain goals in mind.
These types of push-pull movements also dominate computer vision (where many of the "correct" DSP approaches fell away to less-rigid, learned proxies), and language modeling (tokenization is hardly "raw", and byte based approaches to-date lag behind smart tokenization strategies), and I think every field which approaches learning from data will have various swings over time.
CCD bitstreams are also not "raw", so people will continue to push down in representation while making bigger datasets and models, and the rollercoaster will continue.
On the loops / sampling front: I always thought RAVE [0][1][2] was a very interesting approach, that really embraces latent spaces and sample/stretch type approaches in the waveform space
Research into "pure" unconditional generation can often lead to gains in the conditional setting. See literally any GAN research, VQ-VAE, VAE, diffusion, etc - all started from "unconditional/low information" pretty much. Both directly (in terms of modeling) and indirectly (by forcing you to really reason about what conditioning is telling you about the modeling, and what's in the data), these approaches really force you to think about what it means to just "make music".
Also, I think artistic uses (such as Dadabots, who heavily used SampleRNN) show clearly that "musicians" like interesting tools, even if uncontrolled in some cases. Tools to exactly execute an idea are important (DAW-like), but so are novelty generating machines like (many) unconditional generators end up being. Jukebox is another nice example of this.
On the "good for elevator music" comment - the stuff I've heard from these models is rarely relaxing enough to be in any elevator I would ride. But there are snippets of inspiration in there for sure.
Generally, I do favor controllable models with lots of input knobs and conditioning for direct use, but there's space for many different approaches in pushing the research forward.
Different creators will work all kind of odd models into their workflows, even things that are objectively less "high quality", and not really controllable. To me, that's a great thing and reason enough to keep pushing unsupervised learning forward.
This work is another classic in the "neural nets meet spreadsheets" genre [0]. Really helps visualize what is going on in (at least some) latent spaces.
This same technique, extended can work well for detecting plagiarism from the underlying corpus as well, by tracking a trie of "good" completions in the n-gram sense, and a longer trie of "no-good" completions. This technique was (to my knowledge) first shown in [0], and particularly [1] is a really interesting video discussing these topics around max-order grams even in a Markovian setting. I used this technique a bit in symbolic music generation and was quite pleased with the results, always planned to work it into whatever next models.
I think there are a lot of methods from these older Markovian setups that can be employed in the outputs samplers of modern models, as well as the inclusion of structured searches and so on. Parts of deep learning have always focused on structured output search, but historically the LLM style generative setting has not employed these approaches (though I find beam search for generative settings needs tweaking, it usually works pretty well in smaller scale problems for me).
There was a really nice post on doing this kind of thing with CRF back in 2015 [0]. Open source data, and code on github. Also a nice tutorial on structured prediction using CRF type models.
Would be interesting if you could prompt, LoRA distill, or use modern LLM tricks against a well-labeled and curated set, similar to how other tagging problems are handled with modern pretrained models.
I would say in the neural network literature at large, and in audio modeling particularly, this continual back and forth of pushing DSP-based knowledge into neural nets, on the architecture side or data side, versus going "raw-er" to force models to learn their own versions of DSP-style transforms has been and will continue to be a see-saw, as we try to find what works best, driven by performance on benchmarks with certain goals in mind.
These types of push-pull movements also dominate computer vision (where many of the "correct" DSP approaches fell away to less-rigid, learned proxies), and language modeling (tokenization is hardly "raw", and byte based approaches to-date lag behind smart tokenization strategies), and I think every field which approaches learning from data will have various swings over time.
CCD bitstreams are also not "raw", so people will continue to push down in representation while making bigger datasets and models, and the rollercoaster will continue.