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 …

Text-to-music generation models are now capable of generating high-quality music audio in
broad styles. However, text control is primarily suitable for the manipulation of global musical …

Self-supervised learning (SSL) has recently emerged as a promising paradigm for training
generalisable models on large-scale data in the fields of vision, text, and speech. Although …

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 …

This paper presents a novel deep learning model called Self-Attention Layer within a
Convolutional Neural Network (SACNN), specifically designed for detecting acoustic data in …

Synthesize human motions from music (ie, music to dance) is appealing and has attracted
lots of research interests in recent years. It is challenging because of the requirement for …

In the era of extensive intersection between art and Artificial Intelligence (AI), such as image
generation and fiction co-creation, AI for music remains relatively nascent, particularly in …

Music transcription is the process of transforming recorded sound of musical performances
into symbolic representations such as sheet music or MIDI files. Extensive research and …

Transformers and variational autoencoders (VAE) have been extensively employed for
symbolic (eg, MIDI) domain music generation. While the former boast an impressive …