Significant progress has been made in symbolic music generation with the help of deep
learning techniques. However, the tasks covered by symbolic music generation have not …

The utilization of deep learning techniques in generating various contents (such as image,
text, etc.) has become a trend. Especially music, the topic of this paper, has attracted …

Music generation has generally been focused on either creating scores or interpreting them.
We discuss differences between these two problems and propose that, in fact, it may be …

Realistic music generation is a challenging task. When building generative models of music
that are learnt from data, typically high-level representations such as scores or MIDI are …

The sound of the theremin is familiar today, but imagine again going back a century, to the
world described above, and hearing that sonic purity of the two heterodyning oscillators so …

Computational Creativity is a multidisciplinary field that tries to obtain creative behaviors
from computers. One of its most prolific subfields is that of Music Generation (also called …

Vocabulary learning support tools have widely exploited existing materials, eg, stories or
video clips, as contexts to help users memorize each target word. However, these tools …

Machine learning is challenging the way we make music. Although research in deep
generative models has dramatically improved the capability and fluency of music models …

We introduce MIDI-VAE, a neural network model based on Variational Autoencoders that is
capable of handling polyphonic music with multiple instrument tracks, as well as modeling …

In addition to traditional tasks such as prediction, classification and translation, deep
learning is receiving growing attention as an approach for music generation, as witnessed …