Only occasionally though, I’ve been asked those classic questions like “So how did you start your career?”, “What motivated you to start a PhD course?”, etc., and somehow I ended up promising that I’ll write a post about it. So, here we go. Disclaimer: I’ll be only straightforward, simple, and dumb.
My tutor during my high school was kind of my role model and he studied EE. But at the last moment, I applied for something else in engineering school because I was not confident about my exam score. Yeah I failed and spent another year studying. OK I finally got a great score, but I wasn’t still sure if I should go EE. But my teacher (who studied and taught Chemistry) was so sure about EE (because the industry of Korea is very EE-centered) that he almost didn’t listen to me. So.. yeah, zero insight of mine.
Master: Applied Acoustics
I was completely lost during my first 2 years in college and was demotivated even before I tried to study hard. Let’s say I liked bass guitar a lot. During the second semester of my second year, I took this class named Acoustics which completely fascinated me. The professor was about to retire soon though, and that didn’t give me any other choice w.r.t. the mandatory military service etc but starting my master on it right after my graduation so here we go.
3-years of working in a research company
So yeah my supervisor retired one year after I graduated my master’s course. Well, I had to deal with my military service anyway. I could’ve tried something else, but I quite liked Acoustics and there’re not a lot of places that I could keep working on it while serving alternative military service. So yeah.
Starting a PhD
When finishing my master, I explicitly decided I’m not going to do a PhD. I liked the work I did for my master’s thesis (don’t read it) and tried to publish it (which I did, fortunately, and also don’t read it). I then wanted to publish more work (in music information retrieval/MIR), I guess because I wanted to prove myself. I tried. But there was no one I could ask supervision in MIR. My works were rejected from ICASSP – ISMIR – ICASSP – ISMIR in 2 years. I thought it is because I was not getting any supervision (i.e., I blamed my circumstance). Well, therefore, let’s me start a PhD!
At the moment, I had many friends who’s doing their PhD in universities in Korea. I didn’t like the downside of it. I also wanted to experience (lol sorry it might be longer than you think) living outside of Korea. I also wanted to work outside of Korea because in there really was only a limited numbers of choices in Korea in my field, Acoustics, um ok which became my thing because I quite liked it & I had to do my military service & the professor was about to retire & I liked music & etc.. Anyway, these are why.
Acoustics is very fun but many of my colleagues in Applied Acoustic Lab actually started it because they liked music music – even the supervisor – rather than sound. I already knew MIR might be even smaller than applied acoustics/DSP, I knew MIR is definitely smaller than speech, I knew there was no job in MIR in Korea. I don’t know, if I were not to choose what I like, I should’ve gone to a dental school. So what’s the point of compromising at that point? (Actually, there were/are still good reasons to do so, but somehow I didn’t.)
PhD: Computer Science
In the company, I had to run some experiments with many, many speakers etc and it was so painful. I was not even good at it. In the same team, those who were working on audio codec seemed free from those troubles, and that made me want to migrate from the world of EE or (physical) sound to the digital world. They seemed less cumbersome to develop / run experiments / (objectively) evaluate. Just neat. At least relatively neat. Ok, I’m logging in..
PhD: Machine Learning
Some reviewers who rejected my papers said my paper is lack of machine learning practices (which was correct, helpful, and timely). I also realized I couldn’t really comprehend any paper in MIR because I don’t know those things, concepts, symbols, just anything. I started to study about a year before I started my PhD.
PhD: Deep Learning
- I was frustrated a bit by seeing the features from computer vision beat audio features in some audio-related tasks. One of my friends, who I studied and worked together for about 10 years, was also a big fan of things in computer vision. In the end, I was convinced that deep learning would probably work well in music because it was already working very well in vision.
- Another friend of mine recommended taking the Coursera lecture by Hinton.
- I spent my first day of PhD reading Sander’s post about applying ConvNet for audio-based recommendation.
- I wanted to get prepared to quit music after my PhD if the industry prospect wouldn’t look good. To do that, I needed to learn something versatile.
My two cents
- A lot of coincidences and luck has lead me here.
- Some of the limited choices is because I only considered the things I was interested in.
- You better figure out what to do after what you’re doing is done. As early as possible.
- For me, momentum mattered a lot. Once you’ve invested your time, you want to utilize it. In other words, it’s difficult not to be dependent on where you’ve been. In other words, every choice matters.
But you might be different from me.
Hope it was helpful 🙂 or amusing, at least.