Recent advancements in deep generative models present new opportunities for music
production but also pose challenges, such as high computational demands and limited …

Recent advances in text-to-music editing, which employ text queries to modify music (eg\by
changing its style or adjusting instrumental components), present unique challenges and …

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

In this article, we explore the potential of using latent diffusion models, a family of powerful
generative models, for the task of reconstructing naturalistic music from …

Building upon Diff-A-Riff, a latent diffusion model for musical instrument accompaniment
generation, we present a series of improvements targeting quality, diversity, inference …

Large-scale text-to-music generation models have significantly enhanced music creation
capabilities, offering unprecedented creative freedom. However, their ability to collaborate …

The recent emergence of diffusion models has significantly advanced the precision of
learnable priors, presenting innovative avenues for addressing inverse problems. Since …

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 …