  In the domain of algorithmic music composition, machine learning-driven
systems eliminate the need for carefully hand-crafting rules for composition.
In particular, the capability of recurrent neural networks to learn complex
temporal patterns lends itself well to the musical domain. Promising results
have been observed across a number of recent attempts at music composition
using deep RNNs. These approaches generally aim at first training neural
networks to reproduce subsequences drawn from existing songs. Subsequently,
they are used to compose music either at the audio sample-level or at the
note-level. We designed a representation that divides polyphonic music into a
small number of monophonic streams. This representation greatly reduces the
complexity of the problem and eliminates an exponential number of probably poor
compositions. On top of our LSTM neural network that learnt musical sequences
in this representation, we built an RL agent that learnt to find combinations
of songs whose joint dominance produced pleasant compositions. We present
Amadeus, an algorithmic music composition system that composes music that
consists of intricate melodies, basic chords, and even occasional contrapuntal
sequences.
