The question of what makes Bach sound like Bach is, needless to say, not addressed. The actual thing they're reporting is: '“You need to be able to say from 3 seconds and 50 milliseconds to 78 milliseconds, this instrument is playing an A. But that’s impractical or impossible for even an expert musician to track with that degree of accuracy.” The UW research team overcame that challenge by applying a technique called…
Hi savanaly, When we talk about "what makes Bach sound like Bach," the technical concept we have in mind is the recent work in computer vision on style transfer. For example, https://arxiv.org/abs/1508.06576 We are excited to work on adapting these models to the musical domain! As for note prediction, you can see our results in our paper: https://arxiv.org/abs/1611.09827 Our are results for simple (2-layer, not very…
I am starting to record my own dataset for solo jazz piano - all midi though. Monophonic melodies, and matching chord voicings and voice leading from one chord to the next. With the goal of learning to generate a good sounding jazz piano arrangement to a given melody with nothing except monophonic input.
Style transfer is good at essentially texture transfer - I suspect it won't work that well for understanding music theory (or text), especially with long time series dependencies, but will be very curious to see what emerges.
I'd like to hear more generative music samples from DeepMind's WaveNet too, the piano samples they published sounded very good, but it was unclear what the model had learned or generalised - and how much was semi-randomised recall. I haven't seen the open source implementations of WaveNet produce as good results yet - probably because it's computationally very expensive to train and run, and that limits experimentation. I saw Aäron give a talk on it a couple of weeks ago which helped me understand the stacked dilated convolutions - but would still like to hear more music examples :)