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 …

In this work, we define a diffusion-based generative model capable of both music synthesis
and source separation by learning the score of the joint probability density of sources …

In this work, we focus on solving the problem of timbre transfer in audio samples. The goal is
to transfer the source audio's timbre from one instrument to another while retaining as much …

Diffusion-based generative models have recently achieved remarkable results in speech
and vocal enhancement due to their ability to model complex speech data distributions …

In this work, we demonstrate the integration of a score-matching diffusion model into a
deterministic architecture for time-domain musical source extraction, resulting in enhanced …

Timbre transfer techniques aim at converting the sound of a musical piece generated by one
instrument into the same one as if it was played by another instrument, while maintaining as …

Timbre transfer techniques aim at converting the sound of a musical piece generated by one
instrument into the same one as if it was played by another instrument, while maintaining as …

Generative models have experienced significant advancements in recent years, driven by
the introduction of architectures such as Stable Diffusion, GPT-3, ChatGPT, and many …

This thesis proposes a journey into sound processing through deep learning, particularly
generative models, exploring the compositional structure of sound, which is layered in …

Blind signal separation is the task of separating the given mixture signal into two or more
corresponding sources. It finds an application in many fields of human activity such as …