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 …

Personalized recommendation serves as a ubiquitous channel for users to discover
information tailored to their interests. However, traditional recommendation models primarily …

Existing approaches for generating multitrack music with transformer models have been
limited in terms of the number of instruments, the length of the music segments and slow …

We introduce CLaMP: Contrastive Language-Music Pre-training, which learns cross-modal
representations between natural language and symbolic music using a music encoder and …

Benchmark plays a pivotal role in assessing the advancements of large language models
(LLMs). While numerous benchmarks have been proposed to evaluate LLMs' capabilities …

Recently, symbolic music generation with deep learning techniques has witnessed steady
improvements. Most works on this topic focus on MIDI representations, but less attention has …

Deep learning technology has been extensively studied for its potential in music, notably for
creative music generation research. Traditional music generation approaches based on …

Due to the particularity of the simultaneous occurrence of multiple events in music
sequences, compound Transformer is proposed to deal with the challenge of long …

Music plays a vital role in human culture and society, serving as a universal form of
expression. However, accurately classifying music emotions remains challenging due to the …

In the present study, we investigate the supervised problem of composer classification. From
a set of compositions and a set of composers, we seek to assign each composition to the …