21 ms·

> What do you mean by "better" More convincing/realistic musical performance. Imagine there was a "Turing test" for music composition and performance and the g

by Rochus 5y ago

> What do you mean by "better" More convincing/realistic musical performance. Imagine there was a "Turing test" for music composition and performance and the goal was to convince a trained musician that the performer behind the curtain was a true human being, not a robot. > There is a market for "band-in-a-box" Right, and the tool became much better since they use "real tracks", i.e. played by real musicians; I suppose in future these can be complemented by tracks generated by connectionist models like the ones used by MuseNet et al. > see something like Curtis Roads CMT for a historical overview of paradigms. See also Deep Learning Techniques for Music Generation -- A Survey: https://arxiv.org/abs/1709.01620 https://arxiv.org/abs/1709.01620 and Algorithmic Composition - Paradigms of Automated Music Generation: https://link.springer.com/book/10.1007/978-3-211-75540-2 https://link.springer.com/book/10.1007/978-3-211-75540-2

nonrandomstring 5y ago
Ah yes, the performance side of it. That's interesting. And I can see why learned timing and articulation can be used to greatly improve the "realism" of that. You could even have "celebrity" performer profiles that learn Eric Clapton guitar performances the same way that synthesised voice style can be extracted from a speaker corpus. A tool like that would be really fun and something I'd like to play with. It would take my composition and _perform_ the parts: Drums: Ringo Starr, Solo Guitar: Steve Vai, Bass: Sting In music it's quite usual though to separate the composition, arrangement and performance. TBH, what I found with _all_ ML/AI tools in the composition and orchestration/arrangement stages is badly lacking. Not because the "technology is bad" - massive leaps have been made with gradient descent and transformation, in scale, algorithms and hardware, but because the use paradigm doesn't appeal. It's actually one of the biggest, painful learning experiences in my tech career, and I have taken it to heart - for example with the RJDJ startup we pioneered "reactive music" and had a ton of big artists on board, Chemical Brothers, Hans Zimmer, Massive Attack, .. and we made really credible, innovative music that pretty much defined a new genre and market that didn't exist. But it failed [1]. Why? Because people don't want their music that way. We made the classic mistake of thinking we knew what people wanted because _we_ thought it was cool (and it was, and investors poured money into it). I used to excuse the fail by saying "it was ahead of its time". But as I get older I see that hubris alone was the sword we fell on, by thinking we understood how people want to relate to music. There is such a concept as "fit" - I don't know what else to call it - that tech entrepreneurs often seem to miss. That's why I say that a clunky old thing like MMA is actually a "better" tool, for me personally as a pro/am (once working) musician. I hope that makes sense. [1] AFAIK (I am out of the scene now) one of the people still doing this successfully is Matt from Coldcut with their "Ninja Jam" setup.
- Rochus 5y ago
  > In music it's quite usual though to separate the composition, arrangement and performance. Well, that's mostly what classic musicology does (and some Jazz musicologists inspired by classics). At the latest when you only have a lead sheet and the "composer" expects the musicians to add all the rest, the interpretation of the piece is also an essential part of the musical content/"composition". > the use paradigm doesn't appeal It makes sense to first make some proofs of concept (as e.g. done with MuseNet) concerning compositional/performance quality and then adapt the technology to specific use cases. From my humble point of view there was no convincing result in that respect before e.g. BachBot or MuseNet - maybe with the exception of Cope's work, but his algorithms actually require a human selection and optimization step for useful results - it's not fully algorithmic. > Why? Because people don't want their music that way. Having the right ideas is just a small fraction of success; most is pure luck (from my humble experience/observations). But solving an important/interesting problem is not identical with pleasing a mass market. Concerning music composition and performance there are still big unsolved representation problems, i.e. how to model this n-dimensional problem so that all relevant patterns along each subset of dimensions can be properly described and related to other patterns/features. MuseNet has an interesting approach, but it's mostly a spin-off from linguistics and from my point of view still far from what we should have. There is no satisfactory solution since the beginning of music theory and especially since the beginning of the AI era. All we have are only rudimentary projections, makeshifts to have anything at all. That's why a "a clunky old thing like MMA" will never pass a trained musicians "Turing test".
  - PaulDavisThe1st 5y ago
    > That's why a "a clunky old thing like MMA" will never pass a trained musicians "Turing test". Most good musicians will have distinct assessments of other musicians. "Good" here is a metric that involves a combination of time playing, time performing, time spent collaborating and possibly some innate abilities, should such a thing exist. "I really enjoy playing with them, their sense of timing is so great" "They are really good technically but their development ideas just don't go where I go" etc. etc. There's no reason to expect that any computationally based accompaniment system will do any better than the human ones: if it's actually good, it will have a "personality" that won't work for some people; if it doesn't have a "personality", it almost certainly isn't good. There are many exceptionally talented musicians out there, measured by their technical abilities on their instruments. There are far, far fewer musicians that lots of other musicians want to play with.

by Rochus 5y ago

nonrandomstring 5y ago

Ah yes, the performance side of it. That's interesting. And I can see why learned timing and articulation can be used to greatly improve the "realism" of that. You could even have "celebrity" performer profiles that learn Eric Clapton guitar performances the same way that synthesised voice style can be extracted from a speaker corpus. A tool like that would be really fun and something I'd like to play with. It would take my composition and _perform_ the parts: Drums: Ringo Starr, Solo Guitar: Steve Vai, Bass: Sting In music it's quite usual though to separate the composition, arrangement and performance. TBH, what I found with _all_ ML/AI tools in the composition and orchestration/arrangement stages is badly lacking. Not because the "technology is bad" - massive leaps have been made with gradient descent and transformation, in scale, algorithms and hardware, but because the use paradigm doesn't appeal. It's actually one of the biggest, painful learning experiences in my tech career, and I have taken it to heart - for example with the RJDJ startup we pioneered "reactive music" and had a ton of big artists on board, Chemical Brothers, Hans Zimmer, Massive Attack, .. and we made really credible, innovative music that pretty much defined a new genre and market that didn't exist. But it failed [1]. Why? Because people don't want their music that way. We made the classic mistake of thinking we knew what people wanted because _we_ thought it was cool (and it was, and investors poured money into it). I used to excuse the fail by saying "it was ahead of its time". But as I get older I see that hubris alone was the sword we fell on, by thinking we understood how people want to relate to music. There is such a concept as "fit" - I don't know what else to call it - that tech entrepreneurs often seem to miss. That's why I say that a clunky old thing like MMA is actually a "better" tool, for me personally as a pro/am (once working) musician. I hope that makes sense. [1] AFAIK (I am out of the scene now) one of the people still doing this successfully is Matt from Coldcut with their "Ninja Jam" setup.

Rochus 5y ago
> In music it's quite usual though to separate the composition, arrangement and performance. Well, that's mostly what classic musicology does (and some Jazz musicologists inspired by classics). At the latest when you only have a lead sheet and the "composer" expects the musicians to add all the rest, the interpretation of the piece is also an essential part of the musical content/"composition". > the use paradigm doesn't appeal It makes sense to first make some proofs of concept (as e.g. done with MuseNet) concerning compositional/performance quality and then adapt the technology to specific use cases. From my humble point of view there was no convincing result in that respect before e.g. BachBot or MuseNet - maybe with the exception of Cope's work, but his algorithms actually require a human selection and optimization step for useful results - it's not fully algorithmic. > Why? Because people don't want their music that way. Having the right ideas is just a small fraction of success; most is pure luck (from my humble experience/observations). But solving an important/interesting problem is not identical with pleasing a mass market. Concerning music composition and performance there are still big unsolved representation problems, i.e. how to model this n-dimensional problem so that all relevant patterns along each subset of dimensions can be properly described and related to other patterns/features. MuseNet has an interesting approach, but it's mostly a spin-off from linguistics and from my point of view still far from what we should have. There is no satisfactory solution since the beginning of music theory and especially since the beginning of the AI era. All we have are only rudimentary projections, makeshifts to have anything at all. That's why a "a clunky old thing like MMA" will never pass a trained musicians "Turing test".
- PaulDavisThe1st 5y ago
  > That's why a "a clunky old thing like MMA" will never pass a trained musicians "Turing test". Most good musicians will have distinct assessments of other musicians. "Good" here is a metric that involves a combination of time playing, time performing, time spent collaborating and possibly some innate abilities, should such a thing exist. "I really enjoy playing with them, their sense of timing is so great" "They are really good technically but their development ideas just don't go where I go" etc. etc. There's no reason to expect that any computationally based accompaniment system will do any better than the human ones: if it's actually good, it will have a "personality" that won't work for some people; if it doesn't have a "personality", it almost certainly isn't good. There are many exceptionally talented musicians out there, measured by their technical abilities on their instruments. There are far, far fewer musicians that lots of other musicians want to play with.