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We propose a novel approach for the generation of polyphonic music based onLSTMs. We generate music in two steps. First, a chord LSTM predicts a chordprogression based on a chord embedding. A second LSTM then generates polyphonicmusic from the predicted chord progression. The generated music sounds pleasingand harmonic, with only few dissonant notes. It has clear long-term structurethat is similar to what a musician would play during a jam session. We showthat our approach is sensible from a music theory perspective by evaluating thelearned chord embeddings. Surprisingly, our simple model managed to extract thecircle of fifths, an important tool in music theory, from the dataset.

JamBot: Music Theory Aware Chord Based Generation of Polyphonic Music with LSTMs