Although audio generation shares commonalities across different types of audio, such as
speech, music, and sound effects, designing models for each type requires careful …

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 …

Augmenting large language models (LLMs) to understand audio--including non-speech
sounds and non-verbal speech--is critically important for diverse real-world applications of …

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

The growing popularity of generative music models underlines the need for perceptually
relevant, objective music quality metrics. The Frechet Audio Distance (FAD) is commonly …

Text-to-music generation (T2M-Gen) faces a major obstacle due to the scarcity of large-scale
publicly available music datasets with natural language captions. To address this, we …

The current landscape of research leveraging large language models (LLMs) is
experiencing a surge. Many works harness the powerful reasoning capabilities of these …

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 …

With the recent advancement in large language models (LLMs), there is a growing interest in
combining LLMs with multimodal learning. Previous surveys of multimodal large language …

Building on the foundations of language modeling in natural language processing, Next
Token Prediction (NTP) has evolved into a versatile training objective for machine learning …