3 research outputs found
Generating Nontrivial Melodies for Music as a Service
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.Comment: ISMIR 2017 Conferenc
Generating Albums with SampleRNN to Imitate Metal, Rock, and Punk Bands
This early example of neural synthesis is a proof-of-concept for how machine
learning can drive new types of music software. Creating music can be as simple
as specifying a set of music influences on which a model trains. We demonstrate
a method for generating albums that imitate bands in experimental music genres
previously unrealized by traditional synthesis techniques (e.g. additive,
subtractive, FM, granular, concatenative). Raw audio is generated
autoregressively in the time-domain using an unconditional SampleRNN. We create
six albums this way. Artwork and song titles are also generated using materials
from the original artists' back catalog as training data. We try a
fully-automated method and a human-curated method. We discuss its potential for
machine-assisted production.Comment: 3 page
Generating Black Metal and Math Rock: Beyond Bach, Beethoven, and Beatles
We use a modified SampleRNN architecture to generate music in modern genres
such as black metal and math rock. Unlike MIDI and symbolic models, SampleRNN
generates raw audio in the time domain. This requirement becomes increasingly
important in modern music styles where timbre and space are used
compositionally. Long developmental compositions with rapid transitions between
sections are possible by increasing the depth of the network beyond the number
used for speech datasets. We are delighted by the unique characteristic
artifacts of neural synthesis.Comment: 3 page