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Found 15 papers, 4 papers with code
Motifs, Phrases, and Beyond: The Modelling of Structure in Symbolic Music Generation
Modelling musical structure is vital yet challenging for artificial intelligence systems that generate symbolic music compositions.
Exploring XAI for the Arts: Explaining Latent Space in Generative Music
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.
Towards Mode Balancing of Generative Models via Diversity Weights
Large data-driven image models are extensively used to support creative and artistic work.
Trash to Treasure: Using text-to-image models to inform the design of physical artefacts
Text-to-image generative models have recently exploded in popularity and accessibility.
The Beyond the Fence Musical and Computer Says Show Documentary
During 2015 and early 2016, the cultural application of Computational Creativity research and practice took a big leap forward, with a project where multiple computational systems were used to provide advice and material for a new musical theatre production.
Explainable Computational Creativity
Human collaboration with systems within the Computational Creativity (CC) field is often restricted to shallow interactions, where the creative processes, of systems and humans alike, are carried out in isolation, without any (or little) intervention from the user, and without any discussion about how the unfolding decisions are taking place.
Active Divergence with Generative Deep Learning -- A Survey and Taxonomy
Generative deep learning systems offer powerful tools for artefact generation, given their ability to model distributions of data and generate high-fidelity results.
Automating Generative Deep Learning for Artistic Purposes: Challenges and Opportunities
The framework provides opportunities to hand over creative responsibilities to a generative system as targets for automation.
Expressivity of Parameterized and Data-driven Representations in Quality Diversity Search
We consider multi-solution optimization and generative models for the generation of diverse artifacts and the discovery of novel solutions.
Camera Obscurer: Generative Art for Design Inspiration
Using a visual similarity search for image retrieval, the \emph{Camera Obscurer} app enables rapid searching of tens of thousands of generated abstract images of various types.
Exploring Novel Game Spaces with Fluidic Games
We present here a new type of mobile app -- fluidic games -- and illustrate our iterative approach to their design.
Off The Beaten Lane: AI Challenges In MOBAs Beyond Player Control
MOBAs represent a huge segment of online gaming and are growing as both an eSport and a casual genre.
The Digital Synaptic Neural Substrate: A New Approach to Computational Creativity
It uses selected attributes from objects in various domains (e. g. chess problems, classical music, renowned artworks) and recombines them in such a way as to generate new attributes that can then, in principle, be used to create novel objects of creative value to humans relating to any one of the source domains.
Modelling serendipity in a computational context
We conclude that it is feasible to equip computational systems with the potential for serendipity, and that this could be beneficial in varied computational creativity/AI applications, particularly those designed to operate responsively in real-world contexts.
A Survey of Monte Carlo Tree Search Methods
Monte Carlo Tree Search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling.
