Tag Archives: Artificial Creativity

MusAE’s source code on Github

The source code of my latest project, MusAE, is now available on Github. If you’re interested, go check it out! I already spoke (in broad terms) about MusAE in a previous post, the technical details will be available soon.

(Spoiler) You can already read about the details of an early version of MusAE in my Master’s thesis.


Mus̠ РAudio samples available on YouTube

At the following link, you can find a few audio samples generated by MusAE, the Machine Learning model for MIDI music manipulation I am currently working on. The name is an acronym of Music Adversarial autoEncoder, and a tribute to ancient Greek’s goddess of sciences and arts. The link redirects you to a YouTube playlist containing both song reconstructions (MusAE is based on an autoencoder model) and interpolations between two different measures. There is also a medley between Michael Jackson’s “Billie Jean” and Pink Floyd’s “Brain Damage”, which combines the reconstruction and interpolation capabilities of the model.

MusAE’s main goal is to assist new artists (even with limited knowledge of music theory) in creating their new masterpieces by automatically modifying relevant properties of musical pieces, thus leaving the artists free to concentrate only on the creative aspects of music composition, without caring too much about low-level technical nuisances.
This work is just at its initial stages, but I think the result are already quite impressive!

If you’re interested in MusAE, more technical details will be coming soon.