5 ms·

> We can do better What do you mean by "better", and for whom? I've used a _very_ large amount of generative musical software and written a fair bit myself, an

> We can do better What do you mean by "better", and for whom? I've used a _very_ large amount of generative musical software and written a fair bit myself, and these are two different use cases. MMA is a rather decent text markup that can allow a composer to sketch out an idea in terms of style, rhythms, structure, instrumentation, mood, chord inversions, decorations and passing - and then tweak that in a domain that is more or less one-to-one in effect, but still way above the level of editing individual MIDI notes. Contrast that with tools that more or less wipe your arse for you, by churning out very interesting and intelligent "compositions", either via Markov, NN, clustering and reinforcement, genetic and evolutionary processes, tuned stochastic processes, generate and filter methods and so on... BTW, composition is one of the oldest application areas for "AI" around - I was building "Boltzmann and Perceptron Machines" in the 80s to do this on a 286 PC. What I'm saying is the application area is _well_ mapped over the past 50 years [1] The latter methods great for getting inspiration and variations to select from - "play me something more like this in a rock style", but then so is listening to some music and chaning a keyword in an MMA file. For a moderately competent composer an intermediate-high level score, plus a good knowledge of the style libraries, is a far more desirable. There is a market for "band-in-a-box" type products, where people want fast, fun backings to write lyrics to, or some kind of "in the style of" Karaoke machine for playing live at La Vista Del Sol Bar bar, but I can say from experience of working with a lot of _writing_ musicians that it's not what they're after, and it isn't clear if ML/AI can help much with that. [1] see something like Curtis Roads CMT for a historical overview of paradigms.

adriand 5y ago
I’m really interested in hearing more about the programs that you have used and what you would recommend for somebody who wants to retain a lot of creative control. I’m particularly interested in something that would assist with generating small parts. I have no interest in anything that is going to create an entire track for me, because that is an activity I enjoy doing. But if for instance, I had something and I was like, this would really benefit from a high hat loop on top, is there anything that I could use for that purpose? Other than of course going sample hunting?
- nonrandomstring 5y ago
  Adriand, forgive me its such a huge body of stuff that we'd be here for a while (and I've probably forgotten most of the really cool stuff). A great start would be to download the CCRMA linux distro (if that still exists) and I think IRCAM still do one. These are jam packed with various research level toys and frameworks, many of which are amazing takes on algorithmic music generation. Have fun.
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.

adriand 5y ago

I’m really interested in hearing more about the programs that you have used and what you would recommend for somebody who wants to retain a lot of creative control. I’m particularly interested in something that would assist with generating small parts. I have no interest in anything that is going to create an entire track for me, because that is an activity I enjoy doing. But if for instance, I had something and I was like, this would really benefit from a high hat loop on top, is there anything that I could use for that purpose? Other than of course going sample hunting?

nonrandomstring 5y ago
Adriand, forgive me its such a huge body of stuff that we'd be here for a while (and I've probably forgotten most of the really cool stuff). A great start would be to download the CCRMA linux distro (if that still exists) and I think IRCAM still do one. These are jam packed with various research level toys and frameworks, many of which are amazing takes on algorithmic music generation. Have fun.

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.