The discipline of Deep Learning has been recognized for its strong computational tools,
which have been extensively used in data and signal processing, with innumerable …

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 …

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 …

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 plays a significant role in evoking human emotions. Thanks to the quick proliferation
of smartphones and mobile internet, music streaming applications and websites have made …

Music datasets play a crucial role in advancing research in machine learning for music.
However, existing music datasets suffer from limited size, accessibility, and lack of audio …

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 …

Most recommender systems adopt collaborative filtering (CF) and provide recommendations
based on past collective interactions. Therefore, the performance of CF algorithms degrades …

Text-to-Music Retrieval, finding music based on a given natural language query, plays a
pivotal role in content discovery within extensive music databases. To address this …

Deep learning models for music have advanced drastically in recent years, but how good
are machine learning models at capturing emotion, and what challenges are researchers …