Giorgia Cantisani — accepted AI-conference papers

2025

Estimating Musical Surprisal in Audio

Mathias Rose Bjare, Giorgia Cantisani, Stefan Lattner, Gerhard Widmer

ICASSP 2025accepted

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 as a proxy for surprisal in symbolic music. With an appropriately chosen model, the IC of musical events has been shown t…

Cited by 3 Source ↗Scholar ↗

2021

Neuro-Steered Music Source Separation With EEG-Based Auditory Attention Decoding And Contrastive-NMF

Giorgia Cantisani, Slim Essid, Gaël Richard

ICASSP 2021accepted

We propose a novel informed music source separation paradigm, which can be referred to as neuro-steered music source separation. More precisely, the source separation process is guided by the user’s selective auditory attention decoded from his/her EEG response to the stimulus. This high-level prior…

Cited by 0 Source ↗Scholar ↗