51 citations · 123 across the 22 of their papers we have counts for
23 papers
Estimating Musical Surprisal from Audio in Autoregressive Diffusion Model Noise Spaces
Mathias Rose Bjare, Stefan Lattner, Gerhard Widmer
Recently, the information content (IC) of predictions from a Generative Infinite-Vocabulary Transformer (GIVT) has been used to model musical expectancy and surprisal in audio. We…
Estimating Musical Surprisal in Audio
Mathias Rose Bjare, Giorgia Cantisani, Stefan Lattner +1
In modeling musical surprisal expectancy with computational methods, it has been proposed to use the information content (IC) of one-step predictions from an autoregressive model a…
Controlling Surprisal in Music Generation via Information Content Curve Matching
Mathias Rose Bjare, Stefan Lattner, Gerhard Widmer
In recent years, the quality and public interest in music generation systems have grown, encouraging research into various ways to control these systems. We propose a novel method…
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…
Singer Identity Representation Learning using Self-Supervised Techniques
Bernardo Torres, Stefan Lattner, Gaël Richard
Significant strides have been made in creating voice identity representations using speech data. However, the same level of progress has not been achieved for singing voices. To br…