4 papers
Perceptually Aligning Representations of Music via Noise-Augmented Autoencoders
Mathias Rose Bjare, Giorgia Cantisani, Marco Pasini +2
We argue that training autoencoders to reconstruct inputs from noised versions of their encodings, when combined with perceptually motivated losses, yields encodings that are struc…
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…
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…