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by abdljasser2
1569 days ago
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Hello! Immediate use case would be sampling. Say you like a certain sound in a song and would like to use it as a starting point for your own sound patch. I also believe that transfer learning has benefits even for making great sounding instruments in cases where you have access to lots of data. That’s my intuition at least. At the very least, it saves you a lot of memory/bandwith. Instead of having one large model per instrument you only need one large models with a few extra instrument specific weights. |
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Just reusing the original recording of a sample is equivalent to drawing a photorealistic tracing of an image: it represents a ground truth, but it's not illustrated in any particular artistic direction. And this makes the multisample libraries available today akin to "dry references" - they can be convincing as reproductions, some of the time, but you're stitching them together like a collage of photos.
If you throw the sample into a synthesis engine you can push around the parameters, crossfade it into a loop, add some envelopes, modulation and layers, and make it a uniquely stylized instrument, and this is one way to take the source material to a new place by forgoing some realism.
Doing the synthesis through style transfer helps move it in a different direction: it gets outside the bounds of directly sequencing performance parameters and makes the performance a little more like an effect, helping to glue the sound. And I think that could be really cool if applied to arbitrary source material.