7 papers
LiveBand: Live Accompaniment Generation in the Audio Domain
Marco Pasini, Javier Nistal, Ben Hayes +3
We present LiveBand, a real-time system that generates high-fidelity music accompaniments to live audio input, respecting strict causal constraints. Our method trains a causal tran…
Diffusion Timbre Transfer Via Mutual Information Guided Inpainting
Ching Ho Lee, Javier Nistal, Stefan Lattner +2
We study timbre transfer as an inference-time editing problem for music audio. Starting from a strong pre-trained latent diffusion model, we introduce a lightweight procedure that…
LiLAC: A Lightweight Latent ControlNet for Musical Audio Generation
Tom Baker, Javier Nistal
Text-to-audio diffusion models produce high-quality and diverse music but many, if not most, of the SOTA models lack the fine-grained, time-varying controls essential for music pro…
Accompaniment Prompt Adherence: A Measure for Evaluating Music Accompaniment Systems
Maarten Grachten, Javier Nistal
Generative systems of musical accompaniments are rapidly growing, yet there are no standardized metrics to evaluate how well generations align with the conditional audio prompt. We…
Continuous Autoregressive Models with Noise Augmentation Avoid Error Accumulation
Marco Pasini, Javier Nistal, Stefan Lattner +1
Autoregressive models are typically applied to sequences of discrete tokens, but recent research indicates that generating sequences of continuous embeddings in an autoregressive m…
Improving Musical Accompaniment Co-creation via Diffusion Transformers
Javier Nistal, Marco Pasini, Stefan Lattner
Building upon Diff-A-Riff, a latent diffusion model for musical instrument accompaniment generation, we present a series of improvements targeting quality, diversity, inference spe…