JamBot: Music Theory Aware Chord Based Generation of Polyphonic Music with LSTMs
This addresses music generation for AI applications, but it is incremental as it builds on existing LSTM methods with a chord-based twist.
The paper tackles polyphonic music generation by using a two-step LSTM approach that first predicts chord progressions and then generates music from them, resulting in pleasing, harmonic output with few dissonant notes and clear long-term structure similar to a jam session.
We propose a novel approach for the generation of polyphonic music based on LSTMs. We generate music in two steps. First, a chord LSTM predicts a chord progression based on a chord embedding. A second LSTM then generates polyphonic music from the predicted chord progression. The generated music sounds pleasing and harmonic, with only few dissonant notes. It has clear long-term structure that is similar to what a musician would play during a jam session. We show that our approach is sensible from a music theory perspective by evaluating the learned chord embeddings. Surprisingly, our simple model managed to extract the circle of fifths, an important tool in music theory, from the dataset.