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 …

We introduce Jukebox, a model that generates music with singing in the raw audio domain.
We tackle the long context of raw audio using a multiscale VQ-VAE to compress it to discrete …

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 …

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 …

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 …

Symbolic music generation aims to generate music scores automatically. A recent trend is to
use Transformer or its variants in music generation, which is, however, suboptimal, because …

Cross-domain named entity recognition (NER) models are able to cope with the scarcity
issue of NER samples in target domains. However, most of the existing NER benchmarks …

We demonstrate that language models pre-trained on codified (discretely-encoded) music
audio learn representations that are useful for downstream MIR tasks. Specifically, we …

Music arrangement generation is a subtask of automatic music generation, which involves
reconstructing and re-conceptualizing a piece with new compositional techniques. Such a …