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 …

Forecasting of multivariate time series data, for instance the prediction of electricity
consumption, solar power production, and polyphonic piano pieces, has numerous valuable …

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 …

The modeling of artificial, human-level creativity is becoming more and more achievable. In
recent years, neural networks have been successfully applied to different tasks such as …

This paper presents the Jazz Transformer, a generative model that utilizes a neural
sequence model called the Transformer-XL for modeling lead sheets of Jazz music …

Deep generative models such as Variational Autoencoders (VAEs) and Generative
Adversarial Networks (GANs) have recently been applied to style and domain transfer for …

Music is a powerful medium for altering the emotional state of the listener. In recent years,
with significant advancements in computing capabilities, artificial intelligence-based (AI …

Numerous studies in the field of music generation have demonstrated impressive
performance, yet virtually no models are able to directly generate music to match …

In recent years, foundation models (FMs) such as large language models (LLMs) and latent
diffusion models (LDMs) have profoundly impacted diverse sectors, including music. This …