Posted by zhinit 5 hours ago
This would be a cool project to work on. Ideally you would buy some vintage gear and then run the audio through both, but that would be very expensive. You could may be find some vst emulations though and get decent results.
1. you can try to describe a sound with some tags and it will try to generate a sound to capture the feeling of these tags
2.you can feed it with a sound sample and it will try to re-synthesize the sound with its synth engine. Though the end result will usually be just a "re-imagined" version of your input sample.
My guess is the underlying model is not a "deep" model. The main benefit is that the end result is not a wave file, but a list of generated parameters that can be synthesized by the synthplant engine. And now it comes the interesting part: you can tweak these parameters to finetune the generated sound. These parameters have actual meanings (FM ratio, reverb etc.)
Like physical modeling synthesis, the interesting part is to compress the sound to some parameters that you can tweak and generate new sounds
Another approach is VAE, which also you give your some latent embedding, you can tweak the embedding to generate new sound. However the meaning of this embedding is not explicit.
This doesn't really work on instruments like guitars. Open D sounds way different than fretted D on the E string. Timbre changes with position and it's one of the ways I determine where a player's hands are on the neck when I'm trying to play their song.
Timbral differences also exist depending on force, the manner plucked, the already ringing overtones... It's hard to know what you want, but the most natural thing is always going to be some organic variation in the notes in general.
If you have a good ear, you aren't, I don't think, hearing so much the timbral diff in the individual open or fretted notes as much as the fact that a barre chord and an open chord is a different voicing of the same harmony.
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That I didn't get from the text.
0: 128 x 173
1: 64 x 87
2: 32 x 44
3: 16 x 22
4: 8 x 11
Then i used 4 separate channels.
This was somewhat arbitrary due to the local training constraint. This would be a hyper parameter worth tuning if I had time to dig into this more.
I trained this a few month ago and don't remember exactly what I tried before I arrived here, but I only ran the whole process 2 or 3 times because of how long it took to train. Hope this answers your question!