RiffShuffle

My 2020 MEng thesis project, advised by Eran Egozy

The thesis is accessible on DSpace@MIT here: https://dspace.mit.edu/handle/1721.1/127386

Don't expect the code to be too good, it was all written under time pressure

Setup

Versions of things are very not extensively tested.

setup and data preprocessing (run this once)

• set up and activate a virtualenv with Python 3, probably Python 3.6 at least?
• pip install -r requirements.txt
• run fetch.sh to hopefully get all the data from the right places; if something fails, read this script and see if you can track the data down???
• run parse_all.py to parse everything into our format and store it in pickle. Nottingham is very slow, like a few minutes; the others should be a few seconds each. Everything is fast now because I upgraded my dependencies? ¯\_(ツ)_/¯

server/client

• Server: with the virtualenv active, python server.py
• Client: npm install; npm start (yarn will probably work too (I forgot which dependency manager I've been using in which project, I guess this one was npm))

(The computations are simple enough that they could probably be done directly on the client in a WebWorker or something. I did a server/client architecture originally because I wanted to leave the door open to use more advanced machine learning libraries on the backend. That didn't happen, but it's too late now. I mean, I could probably sit down for a few hours to a few days and port all the logic to JavaScript if I felt like it, but...)

data

See usertests. We lost several harmonizations because users selected the wrong melody or uploaded the wrong file: 1-2-3, 2-1-5, 2-4-1 (test number - song number - participant number). Should probably have made the server upload them itself, oops.

We also reordered the songs when presenting them to users in test 2 to get more varied data. The JSON files provided here are in the order used in test 1 and described in the thesis; in the second test, they were presented in the order song1, song4, song2, song3.