Deep learning RNN project that creates a melody one note at a time with Mozart's help.

Visit the MiniMozart website here

Watch a presentation that introduces MiniMozart

See the website github repo to see more info about the website

Data creation

Data created from MIDI files acquired from:

• the Classical Music MIDI dataset from Kaggle
• the Music 21 library's MIDI corpus

Preparing the data for the model:

• melody extracted from the MIDI files
• saved values for pitch and duration for each note in the melody
• melody transposed to C major / A Minor
• removed uncommon rhythms and tuplets
• created 8-note-long sequences for X and the 9th note for y

Model building

We created a multi-output deep learning model using Tensor Flow. We used an LSTM for the first layer, before splitting into pitch and duration paths. Each path had an LSTM layer, a dense layer, and a softmax output layer with dropout layers in between each.

API

The API has two main functions:

• initialize: return an opening 8 note sequence at random from one of Mozart's piano sonatas.
• predict: using our model's predictions, suggest three notes (pitch / duration combinations) that are likely to come next in the sequence (according to Mozart)

Install

Go to https://github.com/sevans47/MiniMozart to see the project, manage issues, setup you ssh public key, ...

Create a python3 virtualenv and activate it:

sudo apt-get install virtualenv python-pip python-dev
deactivate; virtualenv -ppython3 ~/venv ; source ~/venv/bin/activate

Clone the project and install it:

git clone git@github.com:sevans47/MiniMozart.git
cd MiniMozart
pip install -r requirements.txt
make clean install test                # install and test

git remote add origin git@github.com:sevans47/MiniMozart.git
git push -u origin master
git push -u origin --tags

Authors

• @sevans47
• @bendthompson
• @Mizuki8783