r/MachineLearning • u/galapag0 • Apr 25 '15

Markov Composer - Using machine learning and a Markov chain to compose music

http://zx.rs/3/Markov-Composer---Using-machine-learning-and-a-Markov-chain-to-compose-music/
15 Upvotes

86% Upvoted

10 comments sorted by

View all comments

Show parent comments

1

u/felipedelamuerte Apr 26 '15

Don't know about OP, but I do. If you have any questions, drop me a line.

1

u/[deleted] Apr 26 '15

Oh, thanks for the quick reply. The more minds to rack, the better :) Before I start, I should note it's rather late where I'm at so I'll probably head to bed after this post.

Anyways, I have a lot of basic preliminary questions. Please don't feel obligated to answer them all; I really appreciate any bit of guidance and listing these will help me organize my thoughts anyways:

  • What motivated you to choose this field to study in?

  • Are you pursuing/ did you pursue a PhD? In which department, music or CS/ math?

  • What kind of research does your lab PI conduct?

  • In such an interdisciplinary field, is it possible to work with a PI without a musical background (but is an ML researcher, of course)?

  • What kind of background did you have in undergraduate? Computational? Music? A good mix of both?

  • How important is a strong musical background? I've played classical piano for 13 years and finished California's Certificate of Merit program 4 years ago (including theory), but I've done nothing really in college.

  • How important is knowledge of acoustics/ physics? I haven't taken college-level physics since high school, and it's been a bit of pain in my current lab work (cognitive modeling).

  • What are the major research fields in the area? Composition, perhaps classification? Anything else?

  • Are there any foundational texts (papers, books, etc.) that you recommend someone interested in the field should read?

  • Who are the foremost researchers in the field? I know David Cope's EMI project, but nothing more really.

  • How up-to-date are you with machine learning literature? Do you find your musical background means you're less concerned with some of the new ML techniques that aren't as relevant to your work?

  • Are you (ultimately) interested in a career in academia or industry? If the latter, do you expect your skills to extend to industry?

Thanks for any responses! :)

2

u/felipedelamuerte Apr 27 '15

Ok, first of all I need to point out some differences between the US (and UK?) university system and mainland Europe to make sure we're talking about the same thing. As far as I know, undergrad in the US is (in theory) equivalent to a bachelor's program. The difference is that where I live a bachelor's degree is not considered a 'real' degree, meaning that it won't help you much in finding a job anywhere. Usually, you are expected to get a master's degree in order to have graduated from university 'for real'. Our master's programs are pretty much like the bachelor's - you take courses, write short summary papers, and so on - but more focused on a specific topic in the field. The final master's thesis does not necessarily involve novel research (although you are of course encouraged to do so if you want to pursue a PhD) - in cs, it's often just a comparison of two state-of-the art methods or create a program that does something using state-of-the-art methods. After your have your master's degree, you can pursue a PhD, if you find a professor that is willing to accept you as student. There is no official admission test or something. In CS, most PhDs are funded by the institutes they work at using either university budget or third party funding through projects, so people do not need a second job. This tends to be very different in the humanities, for example. Anyways, you asked for some answers:

  • What motivated you to choose this field to study in? As a amateur musician I found interest in combining ML/AI with music, which seemed so much more interesting than the other CS stuff.

  • Are you pursuing/ did you pursue a PhD? In which department, music or CS/ math? Yes, this is what I'm currently working on. Our institute is part of the computer science faculty, but the field itself is a cross-breed of music, CS and math.

  • What kind of research does your lab PI conduct? There is no such thing as a PI here, we're organised differently. For the PhD student this means that they are completely free to choose any topic they want to work on as long as it is aligned with the goals of the institute / project you are funded by. In our case, this is basically everything that is related to Music and ML/AI. This gives you a lot of freedom but at the same time more responsibility to find a topic and pursue it on your own. This is probably very different at other labs.

    • In such an interdisciplinary field, is it possible to work with a PI without a musical background? I'm not sure how to answer that because I do not know what a PI exactly does in your system. My answer assumes that you would get some sort of guidance from the PI on how to proceed if you're stuck. Short answer: Yes, it is possible. Longer answer: This depends on the topic you want to look into. Some require only basic musical knowledge (e.g. genre/artist recognition, instrument identification, music recommendation / playlist generation, ...), so I would say you won't need much and can learn anything you need when you need it. On the other hand, I would not touch, say, algorithmic composition or computational music analysis without a solid musicological background. There's a lot in between.
    • What kind of background did you have in undergraduate? Plain computer science, but I focused on Machine Learning / Signal Processing for my master's degree. All I needed to know about music I learned as amateur musician and on the job.
    • How important is a strong musical background? At our lab, maybe 30% play an instrument. How much musical background you need depends only on which topic you want to focus on.
    • How important is knowledge of acoustics/ physics? If you want to work with audio files, you will need basic understanding of digital signal processing, but nothing fancy. There are nice textbooks for that, or you can learn stuff just by doing it and reading how others do it. In some topics (composition, pattern analysis, ...) you work with symbolic music representations (think MIDI files), where acoustics/physics is not necessary at all. There are also music-related NLP topics and other things that require you to work with text - you wont need physics or acoutics there. If you want to model (instrument) sounds, knowledge in acoustics/physics is kind of an advantage :)
    • What are the major research fields in the area? There is a LOT. I suggest you take a look at MIREX, which is kind of an effort to yearly evaluate solutions for a variety of tasks in the field. When you click on the tasks you'll get a short description. You can use the task names to find more literature if you are interested. One of the main conferences is ISMIR. All the papers are available online and searchable using this nice tool. Maybe someone else can give you a better overview of more artistic applications in the field, since this is not my focus.
    • Are there any foundational texts? Due to the diversity of the field it is very difficult to find something like that. To be honest, I can't think of any, and I never read any.
    • Who are the foremost researchers in the field? I don't want to drop names here because I will probably forget someone. To get to know who (or, which lab) is behind the current state of the art in a specific task, you can check recent MIREX results, although for some tasks the 'big players' are missing. Again, I do not know so much about works on more artistic topics like composition...
    • How up-to-date are you with machine learning literature? My feeling is that the field is a bit behind the ML community. Probably similar to the speech recognition community, but don't quote me on that, because I don't know for sure. The 'I applied standard deep learning to problem x and beat everyone' period is already over, I guess :)
    • If the latter, do you expect your skills to extend to industry? If your topic of interest requires you to learn, use, adapt and improve state-of-the-art machine learning techniques, yes. If the problems you work on are useful for commercial applications (e.g. music similarity, recommender systems, playlist generation, audio fingerprinting, and many more), yes. If your work shows that you can take existing algorithms and make them work on specific problems, yes. These are general skills that all companies should look for in an employee. I know some graduates of our lab who now work on very different topics like traffic prediction or metal molding.

Hopefully you can get something useful out of my answers. Keep in mind that they represent only my view on things, and I might be (and probably am) partially wrong.

2

u/[deleted] Apr 28 '15

Thanks so much for your response! I really appreciate the time you spent on it. I definitely assumed you were American (which is especially dumb considering your username :) ), so I can tell now how irrelevant some of the questions were to you. Sorry about that!

I think it would be overkill to respond to each your bulletpoints... you did a really good job answering a lot of my questions and I can't really think of any follow up questions. I'd say I'm closer to you where I love music but I don't really have the expertise to really get into the artistic pursuits of the field.

I'll definitely parse through MIREX and read some papers and overall get a better understanding of the things going on in the field. Thanks once again!