Currently available reviews in the area of artificial intelligence-based music generation do
not provide a wide range of publications and are usually centered around comparing very …

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 …

Abstract Applying the Generalized Nonsmooth Saddle Point Theorem, we obtain multiple
nontrivial periodic bouncing solutions for systems x¨= f (t, x) ̈x=f(t,x) with new conditions. In …

A great number of deep learning based models have been recently proposed for automatic
music composition. Among these models, the Transformer stands out as a prominent …

To apply neural sequence models such as the Transformers to music generation tasks, one
has to represent a piece of music by a sequence of tokens drawn from a finite set of pre …

Symbolic music understanding, which refers to the understanding of music from the symbolic
data (eg, MIDI format, but not audio), covers many music applications such as genre …

While there are many music datasets with emotion labels in the literature, they cannot be
used for research on symbolic-domain music analysis or generation, as there are usually …

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 …

Recent years have witnessed a surge in the popularity of Machine Learning (ML), applied
across diverse domains. However, progress is impeded by the scarcity of training data due …

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 …