Kodaly Music Hand Signals

PS. I did a rough video on this.

You should probably visit the ‘Tutor’ (data capture application) to get an idea of what I’m talking about before reading on.

This mini-project is a tangent to the ELFQuake project proper, to help familiarize myself with machine learning techniques & tools.

Curwen/Kodaly Hand Signals

After this occurred to me last week, I’ve made some progress in implementation. But first some background.

Solfège Hand Signs

Solfège, Curwen, or Kodaly hands signs are a system of hand symbols representing the different pitches in a tonal scale. They’re used to provide a physical association of a pitch system to help connect inner hearing and reading of pitches with musical performance.

from What Is The Purpose Of Solfege Hand Signs?

In their basic form, these are 7 hand signals that are associated with the notes of Tonic sol-fa notation : do, re, mi, fa, sol, la, ti

They made an appearance in the wonderful 1977 film Close Encounters of the Third Kind :

For the musically inclined among you, only having 7 tones may seem a bit limited – even Close Encounter’s tune uses the do at an octave. I believe Kodaly’s extension of Curwen’s system allows a greater range by holding the sign higher or lower (I think – need to google some more). Also the semitones in between are covered. The diagram above mentions fe, ta, and se, whatever they might be. But for now 7 tones feels like plenty.

The Aim

To build a system capable of recognizing Solfège hand signals and playing appropriate tones.

This has been done before – MiLa: An Audiovisual Instrument for Learning the Curwen Hand Signs – but that system used specialized motion capture hardware. Here the plan is to use a regular webcam.

Right now I’m only thinking of getting to a proof of concept, though given that I’ve got an ESP32-Cam module sitting on my shelf, and TensorFlow Lite Micro is supported, there’s potential for embedded fun further down the line.

The Plan

  1. Acquire a lot of images of the hand signals (with associated labels)
  2. Train a machine learning system with that data
  3. Use the trained model to take hand signal images from a webcam and generate the corresponding tones, in real time

Let me unwrap that, starting with 2.

MNIST

The MNIST database of handwritten digits is commonly used as a benchmark for testing pattern recognition/machine learning algorithms. It comprises a total of 70,000 images with associated labels (0-9), which looks something like this –

Typically you take the training set of 60,000 images, fire them (and their labels) at your learning system for as long as it takes. Then you use the test set of 10,000 images (and labels) to evaluate how good your system is at recognising previously unseen images.

This is isomorphic to the core of what is required to recognize hand signals.

There are a lot of systems coded up to work on MNIST. It’s hard not to see a competitive element where different algorithms are proposed that push the accuracy up a little bit further. Wikipedia lists a bunch of classifiers, with error rate ranging from 7.6% (Pairwise linear classifier) to 0.17% (Committee of 20 CNNS with Squeeze-and-Excitation Networks [?!]).

Where the code is available, it’s typically set up to allow reproduction of the results. You point say train.py at the MNIST image and label training set files, wait potentially a very long time, then point test.py at the test set files, then hopefully soon after some numbers pop out giving the accuracy etc.

While the MNIST database is ubiquitous, various limitations have been pointed out. The elephant in the room is that the fact that a particular system does exceedingly well on MNIST doesn’t mean it’ll be good for any other kind of images. This and other issues were the motivation for Fashion-MNIST, a database of more complex images.

I have absolutely no idea what kind of system topology will work well with the hand signals, they are qualitatively a lot different than handwritten digits. But, if I format my dataset as a drop-in replacement for MNIST, I can pick a wide variety of setups off the shelf and try them out, with no extra coding required (this is also the approach taken with Fashion-MNIST). Parameter tweaking will no doubt be needed, but simple trial & error should cover enough bases.

The MNIST format does look rather arcane, but it shouldn’t take me too long to figure a script to compose the data this way.

Back to the Plan part 1.

Data Acquisition

MNIST has 70,000 images. Even if I could capture one a second, this would still take about 20 hours. Noooo..!

But I’m only aiming for a proof of concept, I will consider that achieved with something like a 90% success rate. Almost ever paper you see featuring machine learning will have a chart somewhere with a curve that starts steep and quickly levels off, becoming virtually flat a little way below some desired goal.

I think it’s reasonable to assume most of the systems that can operate on MNIST-like data will have this characteristic, with size of training dataset on the horizontal axis and accuracy on the vertical.

How many sample images will be needed to get to 90%? Clearly it will depend on the algorithms, but in the general case I have no idea. Lots.

So I need to be able to capture images quickly.

After a bit of futile play trying to get a Python GUI app going I gave up (curse you Wayland!), decided to try Javascript in the browser instead. Which, after what experts might consider excessive time on StackOverflow and not enough on MDN API docs,

I got running as a single-page application.

The capture of images from the webcam was straightforward via a <video> element (although there is an outstanding issue in that I couldn’t get the camera light to go off).

Processing, via <canvas> elements turned out to be a lot more convoluted than I expected, I didn’t find that intuitive at all. ‘hidden‘ is the keyword.

Similarly, it took me a good while to figure out a quick way of saving the final image to file (by addressing a hidden <a> element programmatically).

I started with mouse input on <button> elements but soon realised (as any fule kno) that for speed it had to be the keyboard. But that and the rest was pretty straightforward. Generating the tones was trivial, although my code might not be as considerate to the host as it could be.

A huge advantage of implementing this in a browser (aside from being able to get it to work) is the potential for crowdsourced data acquisition. I’ll tweet this!

It was pretty much an afterthought to try this on a mobile device. When I first tried it on my (Android) phone, Capture didn’t work. It is quite possible I had the camera open elsewhere, but I’m still confused why I could see the video stream. Today I showed it to Marinella on her phone, expecting Capture to fail there too. It worked! I just tried again on mine (making sure camera was off), it worked there too!

Even if it does basically work on mobile, there’s still a snag. Ok, from the desktop I can ask people to zip up a bunch of images and mail them to me or whatever. Doing things like that on a mobile device is a nightmare.

If anyone says their willing to capture a bunch of images, but it’ll have to be on mobile, I’m sure I can set something up to quickly post individual images from the application up to a server.

Onto Plan part 3.

Runtime Application

You make a hand signal to a camera, which is periodically taking snapshots. If a snapshot is recognised with reasonable certainty as being of a hand signal, the corresponding tone is played.

Implementation is very much To Be Decided, I’ve got all the data conversion & model play to do first.

Because the ML code will be built with Python, my original thought was to go with this for an application, like a little desktop GUI. I’ve since gone off this idea (blast your eyes, Wayland!). I sometimes forget that I’m a Web person.

So provisionally I’m thinking I’ll set it up as a service over HTTP. Aaron’s web.py is a fun thing I’ve not played with for a long time.

Back to it

Boring Personal STUFF

I’ve neglected this project badly. Aside from being a first-class procrastinator, I am also prone to getting overwhelmed by things. The latter is what happened here. I was at peak enthusiasm when the Kaggle Earthquake Challenge came along, coincidentally my computers all decided to fail at the same time. Not really a big deal, just needed to get things fixed, didn’t take very long. But it knocked the wind out of my sails. Just couldn’t face it at the time.


Fast forward. I’ve just had a couple of weeks knocked out by Covid, clear now. I do need to chase a contract for $$$s like yesterday, but I’m not quite up to working on someone else’s project just yet. I’ve probably got about 100 unfinished projects I could get back to, software and various lumps of electronics sitting on my shelves. But ($$$s aside), this one stands out a mile as being the most worthwhile. So now I’m ready to get back onto the horse/bicycle/crag.

The Proposition

I’m sure I’ve got something similar in this blog’s description, but the basic idea is to use machine learning to identify patterns of correlation between natural radio signals and seismic events and then attempt to make useful earthquake predictions from radio precursors. I have no illusions about this. I reckon, in the best case, very approximate predictions for a very small proportion of events is possible. It won’t be easy and it will take a lot of time. But given how cataclysmic such events can be, it’s worth a try.

The Plan

There are a handful of separate components needed, at the core: data acquisition, a model, a notification system. I think a reasonable 1000 ft view is that of a control system – inputs, processing, outputs, (validation/)feedback. All of which will need creating and tuning.

I really like fiddling with electronics hardware, have put in many hours work looking at the sensor/data acquisition parts of the system. Very poor use of my very limited cognitive resources. After a long break from this, I can shout at myself :

The novel part of this system is around the model.

I think it makes sense to narrow the geographic scope as much as possible, and ‘near me’ is an obvious choice. I live in northern Italy.

High-quality seismic data is available from the National Institute of Geophysics and Volcanology, INGV. Conveniently, the guy who literally wrote the book on natural radio signals has monitoring equipment, streaming live from up near Turin (VLF.it). Also conveniently, in an unfortunate sense, this is an active seismic region (it was the devastating quake of 2009 around L’Aquila that got me wondering about this…not to mention the one in 1920 that reduced Villa Collamandina to rubble, a village I can see from my balcony).

But I have no idea what the model should look like yet. Early on when I was thinking about this, I had a little lightbulb moment. The convolutional networks have been shown to be really efficient at pulling out salient feature from images. A human-friendly way of representing natural radio signals is as a spectrogram. Those should be receptive to reduction by off-the-shelf shape recognition algorithms. Tricky bit is the long-term temporal axis of radio & seismic data. LSTMs probably won’t hack it, but by now there’s probably an appropriate successor. (Ideally the training/application phases will be concurrent, which is a rabbit hole in my near future).

There is an advantage to putting a project on hold for a while, however inadvertent. The software equivalent of Sun Tzu’s “If you wait by the river long enough, the bodies of your enemies will float by.”. Someone else will figure out the algorithms you need.

If it really needs stating, I’m way behind the curve of developments in Deep Learning. But what I think I’ve gathered from the little experiments I’ve tried is that I can play at very small scale on my mediocre home computer (no GPU), acquire/pre-process the data, perhaps get a proof-of-concept (toy!) model topology together. Scale up onto a Cloud service.

Necessary for that is creating an environment in which to code…remembering how to code… Ok, I’m looking at Python, Tensorflow/Keras and/or PyTorch.

So before I consider even a toy version of anything earthquake-related, I need to gently paddle into the water. Last night I had a Brilliant Idea!

Zoltán who?

The prompt for this was probably Flight of the Bumblebee on the Theremin . (She did two takes – one for the sounds, one for the bee. I initially thought she’d ‘cheated’, using a MIDI theremin for note separation – nope. Just put it through tremolo, got her movements against it perfect).

Ok, so Close Encounters of the Third Kind. And/or, Sound of Music. Do, re, me…

With the Sol-Fa Notation (which seems better known that C, D, E… in Italy, btw), Zoltán Kodály, a 20th music teacher built on Corwen et al’s work to have kids do hand signals corresponding to their role/feeling in the scale.

Well, that would be a cool way of playing an instrument.

Naturally I googled it. Naturally, it’d been done by 2016 : MiLa: An Audiovisual Instrument for Learning the Curwen Hand Signs.

But naaaah! I don’t have access to the paper, but in the abstract it says they used ‘a Leap motion sensor‘. Apparently those are spatial tracking things like the IR etc. used with VR kit.

Why not just use a camera?

Grab a frame from webcam, convert it to 28×28 pixel greyscale, associate with one of the 7 labels. Use one of the models known to work well with the MNIST handwritten digit benchmark dataset. Play the Five Tones.

So I’m now in the process of building OpenCV-Python. Predictably my environment was a mess, Anaconda doesn’t seem to play well with QT/Wayland/Ubuntu.

All being well I can get a script together to tell me what hand shape to hold, a few k images within reasonable time. Find model, train, add bleeps.

I’m talking long before I get onto Tensorflow or whatever. Hey ho. Could wait forever for an environment configuration to float by (wasn’t that the whole point of VMs, Docker etc? But when you need one, float on by…).

Should be straightforward once the environment is set up. Which is the purpose of the exercise.