2 citations · 2 across the 11 of their papers we have counts for
5 papers · 1 filter
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…
Music2Latent: Consistency Autoencoders for Latent Audio Compression
Marco Pasini, Stefan Lattner, George Fazekas
Efficient audio representations in a compressed continuous latent space are critical for generative audio modeling and Music Information Retrieval (MIR) tasks. However, some existi…
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.…
Bass Accompaniment Generation via Latent Diffusion
Marco Pasini, Maarten Grachten, Stefan Lattner
The ability to automatically generate music that appropriately matches an arbitrary input track is a challenging task. We present a novel controllable system for generating single…