30 citations · 47 across the 13 of their papers we have counts for
10 papers · 1 filter
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…
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…
Diff-A-Riff: Musical Accompaniment Co-creation via Latent Diffusion Models
Javier Nistal, Marco Pasini, Cyran Aouameur +2
Recent advancements in deep generative models present new opportunities for music production but also pose challenges, such as high computational demands and limited audio quality.…
Stochastic Restoration of Heavily Compressed Musical Audio using Generative Adversarial Networks
Stefan Lattner, Javier Nistal
Lossy audio codecs compress (and decompress) digital audio streams by removing information that tends to be inaudible in human perception. Under high compression rates, such codecs…
DrumGAN VST: A Plugin for Drum Sound Analysis/Synthesis With Autoencoding Generative Adversarial Networks
Javier Nistal, Cyran Aouameur, Ithan Velarde +1
In contemporary popular music production, drum sound design is commonly performed by cumbersome browsing and processing of pre-recorded samples in sound libraries. One can also use…
DarkGAN: Exploiting Knowledge Distillation for Comprehensible Audio Synthesis with GANs
Javier Nistal, Stefan Lattner, Gaël Richard
Generative Adversarial Networks (GANs) have achieved excellent audio synthesis quality in the last years. However, making them operable with semantically meaningful controls remain…
VQCPC-GAN: Variable-Length Adversarial Audio Synthesis Using Vector-Quantized Contrastive Predictive Coding
Javier Nistal, Cyran Aouameur, Stefan Lattner +1
Influenced by the field of Computer Vision, Generative Adversarial Networks (GANs) are often adopted for the audio domain using fixed-size two-dimensional spectrogram representatio…