Abstract
We demonstrate a pattern-based MIDI music generation
system with a generation strategy based on
Wasserstein autoencoders and a novel variant of pianoroll
descriptions of patterns which employs separate
channels for note velocities and note durations
and can be fed into classic DCGAN-style convolutional
architectures. We trained the system on two
new datasets (in the acid-jazz and high-pop genres)
composed by musicians in our team with music
generation in mind. Our demonstration shows
that moving smoothly in the latent space allows us
to generate meaningful sequences of four-bars patterns.
Keywords
- Machine Learning: general
- Human-Computer Interactive Systems: general
