3 papers
cs.SD2026
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…
cs.SD2025
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…
cs.SD2025
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…