deepjazz is a deep learning project that generates jazz music using recurrent neural networks trained on MIDI files. The repository demonstrates how machine learning can learn musical structure and produce original compositions. It uses the Keras and Theano libraries to build a two-layer Long Short-Term Memory network capable of learning temporal patterns in music. The system analyzes musical sequences from an input MIDI file and then generates new musical notes that follow similar stylistic patterns. The project was originally created during a hackathon and was designed to show how neural networks can emulate creative tasks traditionally associated with human musicians. The repository includes preprocessing scripts for preparing MIDI data, training scripts for building the neural network model, and code for generating new compositions.
Features
- Deep learning system that generates jazz music using neural networks
- Two-layer LSTM architecture for modeling musical sequences
- Training pipeline based on MIDI datasets and music analysis
- Implementation built using Keras and Theano frameworks
- Scripts for preprocessing musical data and training models
- Generation of new musical compositions based on learned patterns