161–170 of 481 posts
Earlier quoted context omitted.
Can the spectrogram image be AI upscaled before transforming back to the time domain?
Yes it exists: https://ccrma.stanford.edu/~juhan/super_spec.html But the issue is not that the spectrogram is low quality. The issue is that the spectrogram only contains the amplitude information. You also need phase information for generating audio from the spectogram
I got an actual `HTTP 402: PAYMENT_REQUIRED` response (never seen one of those in the wild, according to Mozilla it is experimental). Someone's credit card stopped scaling?
This is a genius idea. Using an already-existing and well-performing image model, and just encoding input/output as a spectrogram... It's elegant, it's obvious in retrospective, it's just pure genius. I can't wait to hear some serious AI music-making a few years from now.
Makes me wonder if we will see a generalization of this idea. Just like in a CPU 90%+ of want you want to do can be modeled with very few instructions (mov, add, jmp..) we could see a set of very refined models (Stable difussion, GPT, etc) and all of their abstractions on top (ChatGPT, Rifussion, etc).
Earlier quoted context omitted.
All the AI music I’ve heard so far has a really unpleasant resonant quality to it. Why is that? Can it be removed?
I've done some work on AI audio synthesis and the artifacts you're hearing in these clips are coming from the algorithm that is used to go from the synthesized spectrogram to the audio (the Griffin-Lim algorithm). Audio spectrograms have two components: the magnitude and the phase. Most of the information and structure is in the magnitude spectrogram so neural nets generally only synthesize that. If you were to look…
This is really cool but can someone tell me why we are automating art? Who asked for this? The future seems depressing when I look at all this AI generated art.
Because tons of people want to make art, and a lot of art currently requires years of training to make anything close to "good". Making art more accessible to create is a boon to everyone who's dreamed of being able to make their own paintings and music, but doesn't have the skills required.
This really is unreasonably effective. Spectrograms are a lot less forgiving of minor errors than a painting. Move a brush stroke up or down a few pixels, you probably won't notice. Move a spectral element up or down a bit and you have a completely different sound. I don't understand how this can possibly be precise enough to generate anything close to a cohesive output. Absolutely blows my mind.
You can also add another neural-network to "smooth" the spectrogram, increase the resolution and remove artefacts, just like they do for image generation.
Earlier quoted context omitted.
Because tons of people want to make art, and a lot of art currently requires years of training to make anything close to "good". Making art more accessible to create is a boon to everyone who's dreamed of being able to make their own paintings and music, but doesn't have the skills required.
That just means there is going to be a whole lot more bad art in the world
Earlier this year, graphic designers, last month it was software engineers, and now musicians are also feeling the effects. Who else will AI make looking for a new job?
Another related audio diffusion model (but without text prompting) here: https://github.com/teticio/audio-diffusion