Search Results for author: Jack Armitage

Found 3 papers, 2 papers with code

Notochord: a Flexible Probabilistic Model for Real-Time MIDI Performance

1 code implementation18 Mar 2024 Victor Shepardson, Jack Armitage, Thor Magnusson

Deep learning-based probabilistic models of musical data are producing increasingly realistic results and promise to enter creative workflows of many kinds.

A Context-Sensitive Approach to XAI in Music Performance

no code implementations5 Sep 2023 Nicola Privato, Jack Armitage

The rapidly evolving field of Explainable Artificial Intelligence (XAI) has generated significant interest in developing methods to make AI systems more transparent and understandable.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1

Exploring XAI for the Arts: Explaining Latent Space in Generative Music

1 code implementation10 Aug 2023 Nick Bryan-Kinns, Berker Banar, Corey Ford, Courtney N. Reed, Yixiao Zhang, Simon Colton, Jack Armitage

We increase the explainability of the model by: i) using latent space regularisation to force some specific dimensions of the latent space to map to meaningful musical attributes, ii) providing a user interface feedback loop to allow people to adjust dimensions of the latent space and observe the results of these changes in real-time, iii) providing a visualisation of the musical attributes in the latent space to help people understand and predict the effect of changes to latent space dimensions.

Music Generation

Cannot find the paper you are looking for? You can Submit a new open access paper.