  This paper presents a generative AI model for automated music composition
with LSTM networks that takes a novel approach at encoding musical information
which is based on movement in music rather than absolute pitch. Melodies are
encoded as a series of intervals rather than a series of pitches, and chords
are encoded as the set of intervals that each chord note makes with the melody
at each timestep. Experimental results show promise as they sound musical and
tonal. There are also weaknesses to this method, mainly excessive modulations
in the compositions, but that is expected from the nature of the encoding. This
issue is discussed later in the paper and is a potential topic for future work.
