Title: Generating Nontrivial Melodies for Music as a Service,
Abstract: We present a hybrid neural network and rule-based system that generates pop
music. Music produced by pure rule-based systems often sounds mechanical. Music
produced by machine learning sounds better, but still lacks hierarchical
temporal structure. We restore temporal hierarchy by augmenting machine
learning with a temporal production grammar, which generates the music's
overall structure and chord progressions. A compatible melody is then generated
by a conditional variational recurrent autoencoder. The autoencoder is trained
with eight-measure segments from a corpus of 10,000 MIDI files, each of which
has had its melody track and chord progressions identified heuristically. The
autoencoder maps melody into a multi-dimensional feature space, conditioned by
the underlying chord progression. A melody is then generated by feeding a
random sample from that space to the autoencoder's decoder, along with the
chord progression generated by the grammar. The autoencoder can make musically
plausible variations on an existing melody, suitable for recurring motifs. It
can also reharmonize a melody to a new chord progression, keeping the rhythm
and contour. The generated music compares favorably with that generated by
other academic and commercial software designed for the music-as-a-service
industry.