Moved to http://solar-10.wpi.edu/ModalObjectLibrary/deepharmony
Deep Harmony is a machine learning project for creating harmonies based on a given melody.
Using LSTM neural networks to produce accompaniments for melodies. Developed for a music practicum class at WPI.
Currently, Deep Harmony is learning to mimic the behavior of Bach Chorales created by David Cope.
Deep Harmony is written in Python 3.5 and uses the Keras machine learning library.
- Jupyter Notebook, an interactive programming environment and editor (necessary for using the .ipynb files)
- Python 3.5, and the following libraries:
- numpy and matplotlib, for numerical computation and graphical plotting.
- musescore 2+, for allowing music21 to render music as pictures
Jupyter Notebook, Python, Numpy, and Matplotlib are all packaged together in Anaconda, and we recommend installing them that way.