3 ms·

> 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 mu

by 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.
- TheOtherHobbes 5y ago
  "Good" is context dependent, so any credible backing generation system would have to be aware of context - ideally while playing with others. Which is a whole other level of difficulty compared to pumping out canned MIDI files. In any case, studio session musicians have a very different set of skills to pop headliners. Arrangers have different skills to composers. Producers have some of all of the above, but are there to bridge the differing creative pulls of the artist and the record company and manage the project. Label executives and A&R set goals and make high level editorial choices. When the public hears a finished track, it will have been worked on by tens or sometimes hundreds of people. Some of them - image and marketing consultants, PR people, social media managers - have no musical input of any kind, but still shape the subjective experience of the product. As you say, all of the above are paid because they bring a personality and a unique creative flavour to a project. For a culturally significant and/or successful project, each flavour has to be distinctive but also has to blend successfully. Music is always a composite product. Headline talent only gets you so far. The rest comes from creative tensions between individuals with different goals working in different domains. For most styles - including classical - the public wants a narrative/image/hook fantasy that locates the product in a credible cultural space. Generative music has almost none of this. Even if you create a perfectly polished pop single it will be culturally two dimensional because all of the surrounding context is missing. It will be flavourless - not just musically (most likely) but also culturally. In fact there are spaces where this either doesn't matter much or (rarely) doesn't matter at all. But they're small, specialised, and not particularly interesting to work in. Influential, interesting, highly paid projects demand strong and identifiable personal flavours. And they're far harder to synthesise than musically credible tracks/pieces.
  - PaulDavisThe1st 5y ago
    > It will be flavourless - not just musically (most likely) but also culturally. Actually, I doubt this very much. I think there's a tendency to overestimate the "special" that actual humans bring to artistic creation and underestimate what contemporary machine learning (or I prefer to call it, overcooked cross-correlation analysis) can bring. The problem is not so much that something generated using such techniques will be flavorless. It's that once it's good enough, it will have a distinct flavor, just like human artists, and that flavor won't sit well with everyone. That is to say, you can't solve this once: you have to solve it for every human aesthetic preference (within limits). I can entirely believe in an automatic drummer that is "as good" as Steve Gadd, having been trained on the performances of said master percussionist. However, if you want Gavin Harrison instead, you need a different instance. And if you want something new, let's say 20% Vinnie Colaiuta, 30% Phil Collins and 40% Bill Bruford and 10% John Bonham, that will be very to accomplish through ML-style techniques. By contrast, Gavin Harrison could probably do that if you asked him nicely and paid him enough :)

by Rochus 5y ago

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.

TheOtherHobbes 5y ago
"Good" is context dependent, so any credible backing generation system would have to be aware of context - ideally while playing with others. Which is a whole other level of difficulty compared to pumping out canned MIDI files. In any case, studio session musicians have a very different set of skills to pop headliners. Arrangers have different skills to composers. Producers have some of all of the above, but are there to bridge the differing creative pulls of the artist and the record company and manage the project. Label executives and A&R set goals and make high level editorial choices. When the public hears a finished track, it will have been worked on by tens or sometimes hundreds of people. Some of them - image and marketing consultants, PR people, social media managers - have no musical input of any kind, but still shape the subjective experience of the product. As you say, all of the above are paid because they bring a personality and a unique creative flavour to a project. For a culturally significant and/or successful project, each flavour has to be distinctive but also has to blend successfully. Music is always a composite product. Headline talent only gets you so far. The rest comes from creative tensions between individuals with different goals working in different domains. For most styles - including classical - the public wants a narrative/image/hook fantasy that locates the product in a credible cultural space. Generative music has almost none of this. Even if you create a perfectly polished pop single it will be culturally two dimensional because all of the surrounding context is missing. It will be flavourless - not just musically (most likely) but also culturally. In fact there are spaces where this either doesn't matter much or (rarely) doesn't matter at all. But they're small, specialised, and not particularly interesting to work in. Influential, interesting, highly paid projects demand strong and identifiable personal flavours. And they're far harder to synthesise than musically credible tracks/pieces.
- PaulDavisThe1st 5y ago
  > It will be flavourless - not just musically (most likely) but also culturally. Actually, I doubt this very much. I think there's a tendency to overestimate the "special" that actual humans bring to artistic creation and underestimate what contemporary machine learning (or I prefer to call it, overcooked cross-correlation analysis) can bring. The problem is not so much that something generated using such techniques will be flavorless. It's that once it's good enough, it will have a distinct flavor, just like human artists, and that flavor won't sit well with everyone. That is to say, you can't solve this once: you have to solve it for every human aesthetic preference (within limits). I can entirely believe in an automatic drummer that is "as good" as Steve Gadd, having been trained on the performances of said master percussionist. However, if you want Gavin Harrison instead, you need a different instance. And if you want something new, let's say 20% Vinnie Colaiuta, 30% Phil Collins and 40% Bill Bruford and 10% John Bonham, that will be very to accomplish through ML-style techniques. By contrast, Gavin Harrison could probably do that if you asked him nicely and paid him enough :)