the other thing i would note is that there is not a right or wrong way of coloring a piece with dynamics. its a huge area for artistic choice. there doesn't seem to be a great nomenclature for really detailed dynamics in the western music tradition, so it hasn't been able to be recorded on written scores, but i think it can be as much (or more!) part of the composition as the pitches are. a solo snare drum piece would be a good example of this. whats cool about what you did is that it gets away from monotonous dynamics that are the default when you write stuff on a daw by hand.
my own personal approach to this problem has been to treat dynamics as compositional elements independent from pitches or rhythms, and realize this though code. its a pain in the ass to explore dynamic ideas when you have to change all the note velocities by hand, like you said, so i decouple the dynamic curves / sequences / ??? and map them on to different regions of pitches and rhythms. its nice to add a breathing quality to a line by adding some kind of cyclical dynamic, then also being able to add a crescendo on top of that, or being able to decrescendo while still maintaining some kind of pattern of accents or dynamic phrasing. and then being able to change all that quickly, instead of by hand.
Love that. As a singer that's what I've trained all my life, even when it's a just a single marking on the page, you read it within the context of, is this on a phrase, what's the emphasis of the words/syllables, how does it relate to the pitch of the line (e.g. a lot of time a dim on an ascending line merely means "don't be an amateur and get louder"), what's happening with the other voices/instruments. Nuancing a crescendo from one large section to another while also giving respect to phrases within. Fun challenges.
So half of Bach's music was dead when he wrote it?
Both of the performances in the demo do a mediocre job with the shapes in the music, including the phrasing and dynamics.
I suspect more people would be able to hear a clear difference if a more representative human performance was used for the comparison.
As is often the case with ML in music, the bar is higher than it seems to be.
your comment seems to imply intentional misrepresentations
the thing about recordings of performances of music from these periods and composers is that the music is public domain but the performer can copyright the performance
if the human performance midi recordings dataset used in the thesis was legally able to also include the performances by Valentina Lisitsa, Pollini, and Horowitz i am unable to see how the net would fail to make use of their contribution
also for the best results those performers would need to be involved in the production of those midi files because they carry with them a lot of a subjective meta information
i commend the human performers in the available midi files for their effort both in their expression of the piece as well as their desire to make music accessible in a verbose spec'd digital data standard
regardless i feel the real impressive part of the thesis is taking the droned midi and altering it to sound like the human midi.. which i believe is the point moreso than the lcd wow! effect of an, author defined, 'musical turing test'
i mean, really.. access to: the thesis, a full blog write up, repo containing code and a jupyter notebook, and the dataset used;
this work was excellent and the write up phenomenal
I couldn't find it – did OP say whether or not respondents were musicians or not?
Thanks for the comment! The respondents weren't musicians. The respondents were selected randomly as I just wanted to see if StyleNet could fool the average person. However, I will definitely be performing surveys on musicians as I continue my work with StyleNet.
"The jazz ones are very obvious. Also the voicing of the parts in the first chunk of songs was more nuanced with the human than the AI. The jazz is too straight in the runs with the AI, too "perfect". Real life players stall/hitch, even if just a little! But really, it's pretty impressive - way better than old school canned midi player stuff!"
How effective do you think this approach can be with altering timings to try and imitate that style?
Learning timing imperfections with my current setup shouldn't be too difficult to implement. Considering that it can predict velocity quite well, I assume that it would be able to pick up timing too. It's definitely the next thing I plan on experimenting with.
Even if you had a signal which compressed to itself, it seems to me that there would likely be other possible signals which would compress to an identical compressed signal.
Not quite. There is only a ~2% chance that those ~72 people who made a choice got such a good result (62% correct) by random guessing. Still an impressive result.