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 …

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

Editing signals using large pre-trained models, in a zero-shot manner, has recently seen
rapid advancements in the image domain. However, this wave has yet to reach the audio …

Recent advances in text-to-music generation models have opened new avenues in musical
creativity. However, music generation usually involves iterative refinements, and how to edit …

Creating music is iterative, requiring varied methods at each stage. However, existing AI
music systems fall short in orchestrating multiple subsystems for diverse needs. To address …

Instruction-guided speech editing aims to follow the user's natural language instruction to
manipulate the semantic and acoustic attributes of a speech. In this work, we construct triplet …

We present COCOLA (Coherence-Oriented Contrastive Learning for Audio), a contrastive
learning method for musical audio representations that captures the harmonic and rhythmic …

Multi-Source Diffusion Models (MSDM) allow for compositional musical generation tasks:
generating a set of coherent sources, creating accompaniments, and performing source …

The rapid advancement of large language models (LLMs) and multimodal learning has
transformed digital content creation and manipulation. Traditional visual editing tools require …

Audio production style transfer is the task of processing an input to impart stylistic elements
from a reference recording. Existing approaches often train a neural network to estimate …