Implementation of the MiniBach model described in the book Deep Learning Techniques for Music Generation.
The MiniBach model is very simple (a feedforward network with only one hidden layer) so it can be considered a kind of Hello World for symbolic music generation. It is perfect for people just starting with deep learning. For the not so beginners on deep learning, maybe the encoding of the music score provides some insight. In any case, I couldn't find an implementation of the model when I saw it on the book, and decided that it would be a nice contribution to the community.
The implementation is written in a tutorial-like fashion spread out in four notebooks:
- Part 1: Taking a collection of
Humdrum **kernsymbolic music files (Bach chorales) and process them into 4-measure chunks of training data - Part 2: Encoding the 4-measure chunks of training data into one-hot-encoded vectors for the neural network, separating the soprano (input) from the three lower voices (output)
- Part 3: Training the neural network and predicting the accompaniment for one of the melodies in the training examples
- Part 4: Generating the accompaniment for an arbitrary melody
If cloning the repository, clone recursively to get the submodule with the Bach chorales, which are provided by Craig Sapp:
git clone https://github.com/napulen/MiniBach.git --recursive
The music processing is done using music21. The network is implemented with tensorflow, and pandas dataframes are used a few times.
More details about this and other (more advanced) models can be found in the book:
Briot, Jean-Pierre, Gaëtan Hadjeres, and François Pachet. 2017. “Deep Learning Techniques for Music Generation - A Survey.” CoRR abs/1709.01620. http://arxiv.org/abs/1709.01620.
Enjoy!