Python software for tracking musical performances on iPads with Charles Martin's Metatone apps.

You probably already have python on your system, but on OS X, I use homebrew to get a better version. After installing Homebrew, I run:

brew install python

Then I would install some Python packages:

pip install pandas scikit-learn scipy matplotlib tornado
pip install -e git+https://github.com/Eichhoernchen/pybonjour.git#egg=pybonjour

For performing:

cd web-classifier
python metatone-server.py

For debug:

cd MetatoneClassifier/classifier
python setup.app py2app -A
dist/MetatoneClassifier.app/Contents/MacOS/MetatoneClassifier

For packaging:

cd MetatoneClassifier/classifier
python setup.app py2app
open dist/MetatoneClassifier.app

After the performance, you can check the logs in the "classifier/logs" directory.

• classifier-creator: given an input of gesture frames matched with target gestures, this script trains a Random Forest Classifier and saves it as a pickled Python object for the classifier software to run.
• classifier: Performance server software for real-time classification of Metatone app performances.
• converters: scripts to convert logs from one style to another and generate CSV files from general logs.
• generative-classifier: A "fake" version of the MetatoneClassifier using a generative model for gestures instead of the performer's actions. This is used as a control in performance studies.
• modules: Python modules for the project. Including an updated version of OSC.py.
• visualisation: Processing scripts for creating videos of the touch logs
• web-classifier: A web-based version of the classifier software using websockets.