I have recently applied to two PhD programs (in Europe) under professors working in the area of Sound and Music Computing (SMC). Both programs are very well regarded in this area.
SMC covers topics such as:
- Music information retrieval
- Computational musicology
- Algorithmic composition / performance
- Sentiment and expression modelling in music
- Computational approaches to music cognition
- Audio signal processing
This is not a complete list. I find the field very interesting.
However, would the industry want to hire someone with a PhD in SMC? Let's say Google (just as an example), which is known to hire many PhDs. Would they be interested in someone with a PhD in SMC, or would they restrict themselves to machine learning / web / search PhDs? And yes... I'm sure that Yamaha would love to hire an SMC PhD, but would I be restricting my options to only a small subset of the industry?
What about academia? How would they look upon an applicant to the assistant professor position who has a PhD in this area? Would they simply reject him saying "Sorry, but we don't do research in your area...", since it is true that most CS departments don't do active research in SMC? Again, would I be restricting my options to only a small subset of universities?
Secondly, how easy / hard is it to switch fields AFTER doing a PhD in it? I may like SMC enough to work on it for 5 years (and get a PhD) but I MAY not want to work on it for a lifetime. In industry or academia, could I switch to something else when (and if) I want to?
One researcher told me that I should only consider pursuing my interest in SMC after I have already established myself in some other, more fundamental, area of CS, like algorithms or AI. Do you agree?
This thread should be useful to anyone considering a PhD in a specialized or maybe even an obscure area.
SMC sounds like a very interesting but somewhat narrow specialization. Since it's not likely that an expert will appear to answer this question, here are some comments:
One key question regarding career paths is how bad you would feel if they fell through. For example, suppose you got a Ph.D. in SMC, tried and failed to get an academic job in this area, and ended up getting a job in industry that was relevant but didn't really require the full Ph.D. Would you regret having started on this path, or would you be happy to have had a chance to study something exciting and to have developed expertise that might still serve you well in the future? If you would regret it, then you need to think very carefully about the job market, but if your personality could handle this situation, then this path could be a wise choice.
The big danger with unusual fields is that few people will be specifically looking to hire in this area. You may get lucky and find someone who is, but you may need to create your own opportunities. If you have an outgoing personality and are good at networking and making connections, then there will be less risk.
In academia, you'll run into two difficulties. One is that no CS program will need to have this area represented, so you'll have to make a stronger case for why you would be a great hire. The other problem is that if a school is open to SMC, then they may already have someone in this area, and it's a narrow enough field that making multiple hires could be a very tough sell. So you would be looking for the schools that are interested, but not so interested that they have already hired someone. Of course, it's far from impossible, but some other branches of CS may be a little easier.
As for switching areas, it can certainly be done. You may run into a little resistance, depending on what you were originally hired to do. (This could be a serious issue in industry, and even in academia your colleagues may be counting on you to teach the introductory course in your old field.)
If you are equally interested in and talented at algorithms and SMC, then it's probably a little safer to start with algorithms. However, if only one of them will make you happy and inspire you to do your best work, then that one would be the better choice.