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Tempo experiment analysis: the ugly

Previous posts discussed good and bad things about the tempo experiment. This one talks about the ugly stuff.

The fundamental "ugly" thing is that I'm not finished the improved tempo detection algorithm; I had really thought it would be done by now. I started playing with preliminary data halfway through the experiment, and I really thought that I could get everything ready, and just make some final tweaks once I had the complete data.

Two things interfered with this. First, our lab has been working on a simulation of a violin -- we have a set of equations (now in a series of C++ objects) which describe the motion of a violin (mainly the strings) when plucked or bowed. You can write a series of instructions (like "put a finger 0.206299 of the string-distance away from the nut (i.e. "a high second finger"), and bow on the D string with a force of 0.5 Newtons, with an upbow moving at 0.3 meters per seconds), and then the computer generates the resulting audio. I even added python bindings with SWIG! :) Very exciting, very fun... but sadly, very much not helping the tempo detection. :(

The second problem is that I noticed patterns in the data (well, "noticed" is too strong a word -- you'd have to be blind not to see the almost-normality!). That prompted a huge review of statistics. I'd forgotten literally everything from the second-year STAT 270 course I took back in 1998. I knew there was something called a normal curve, which had a bulge in the middle... but I didn't remember that it was also called a Gaussian curve, or what variance was (other than "something to do with the range of data"). So I've had a merry romp through kurtosis, the KS-test, the Shapiro-Wilks test, R, fitdistr(), Q-Q plots, scipy.stats, Cook's distance, the hat matrix, studentizing, etc. I worked in a combination of R, scipy, gnuplot, and matplotlib. I didn't actually use Rpy, although I should have.

But after going around in circles at least three times, I think I'm still back at where I started. I mean, yes, there is insufficient reason to reject a normal distribution for most of the taps-games... but for some games, an obviously good tempo produces residuals which can't reasonably be described as being from the normal distribution, while some other tap-games with an obviously bad tempo have tap-errors which appear to be quite normal. I think the whole statistical approach was a red herring. :(

On the plus side, I now have some practical experience in analyzing experimental data. I kind-of wish I did this in lab courses in 1st and 2nd year of my first degree... granted, philosophy doesn't tend to have many labs, but I spent a lot of my time taking courses outside my major anyway. I even did first-year physics and chemistry, but (regrettably) I only did the lecture courses, not the lab courses.

Well, that's life in an highly interdisciplinary area like music technology. Or at least, that's life in a highly interdisciplinary area if nobody is around to collaborate... I mean, in this field, you either find experts in other fields to cover your weak spots, or you have to learn everything yourself. So far I've been doing everything myself, which is not particularly ideal. :| I guess that once I'm a professor, it'll help me supervise students in the more experimental side of music technology.

Come to think of it, I had exactly the same problem with the violin equations -- I've never dealt with linear density or the second moment of area before, nor had I done any serious digital signal processing programming before. Simply doing things like correctly writing a mathematics equations into code like:

Y1[n-1] = -((w_n + r_n*r_n / w_n) * sin(w_n*dt)) * exp(-r_n*dt);

was a good, albeit time-consuming, experience. And dealing with all the off-by-one errors with math-indexing vs. C-array-indexing was fun... until I finally declared a standard and stuck with it. (I decided to start all for loops with n=1, but do all array indexing with [n-1]. Using n as a loop counter seems wierd to my CS-background, but this is for nodal synthesis, and all the papers I was working from used "sum over all n", and keeping your code syntax as close to the math as possible definitely reduces the chance of silly errors!).

Anyway, on to some pretty pictures. Although it would be nice to trust 971 tap-games (the tap-games where the player either agreed with the tempo, or specified their own), I'm still going through these manually and double-checking them all. So far (567 tap-games examined) I've found 7 tap-games where I don't believe the player's judgement. I'll be asking our local professor emeritus of music to look at those.

So, some data from the doubly-agreed 560 tap-games.


Looks pretty normal, right? At least, it did to me. But none of the normality tests in R and scipy thought it had any reasonable chance of being normal, and upon closer examination I had to admit that the tails were a bit heavier than a normal curve.

But we can still see some interesting things. For example, even this collection of the "most easily identified" tempi (i.e. the most accurate tap-games), there's still a significant amount of taps that are 100 milliseconds away from the correct time. And remember that we're fitting the tempo to give the lowest possible errors -- if we had a set tempo, the errors would be worse. Granted, there aren't all that many taps that are 100 milliseconds away... but 50 milliseconds is certainly within reasonable bounds.

On the question of "why not look at a set tempo" -- due to technical limitations in flash, this is not possible. Flash 10 (and below) can't generate a stable timer, so the visual metronome was created as an animated gif. The flash app doesn't actually know when the gif starts flashing, so unfortunately I can't make any guess about user-tap-times vs. metronome-times. I might try to duplicate portions of this experiment using a controlled setup in the lab to investigate that question.

Oh, one thing that definitely is not interesting is that the residuals are evenly split between positive and negative ("have a skewness very close to 0" in stats terms). That comes from the linear regression. All the reasons against measuring these against a set tempo also apply to measuring them against a set offset (or "intercept" in stats-talk). Now, it would be really interesting to see the distribution of residuals... but again, that requires a controlled lab setup. And a controlled lab setup requires people to be physically present, probably with paid participants, etc.

Hey, I just had a nice thought -- I could set up one of my old netbooks as a rhythm testing platform. It would have a reliable timer, etc. Then whenever I travel places, I take it along with me, and whenever I find a willing victim, I whip it out and they play with it. I'd need ethics approval for a long-term experiment (say, a year?), with me probably finding two or three participants each month... but if each person plays it for 15 minutes, that should give me enough data to see whether people tend to be ahead or behind the actual times.

Anyway, just looking at the residuals isn't enough. Maybe there were just a few tap-games that were wildly incorrect, and that's skewing the data. That's not the case here -- I mean, yes, there were some tap-games that looked pretty wild, but those aren't included in this current data set. But it's a reasonably question to ask, and easily answered by looking at the "average" amount of error. In particular, the standard deviation (also called "root-mean-squared-error").


So... it seems that 4 or 5 tap-games had an "average" error of less than 10 milliseconds, but the vast majority of these tap-games had a RMSE between 15 and 60 milliseconds. That's interesting to note -- I wasn't expecting it to be so high, and most people I've mentioned it to have been surprised that there would be so much variation. But even that figure is a low estimate -- I expect the actual range to be much higher once I include the entire dataset.

This is good to know for anybody working on rhythm grading -- if this experiment is an accurate representation of your target group, then you should expect the best people to have an RMSE between 15 and 25 ms. It's interesting to note that 15-25ms is the just noticeable difference of two sharp onsets. If you record a clap (or any abrupt sound), make a copy, and play the delayed copy 15 ms later than the first, most people will only hear one clap. If you play it 25 ms later, most people will hear two claps. I doubt that there's actually a connection between these two things, but it's interesting to note anyway. :)

Anyway, that's what I have so far. I'm going to force myself to take a break for a day or two, then tackle it again with a fresh outlook. Hopefully I'll be finished the improved algorithm next week, so that I can move on to the rhythm grading experiment! (and write this one up for publication, of course)