This paper is a survey and an analysis of different ways of using deep learning (deep
artificial neural networks) to generate musical content. We propose a methodology based on …

Generative deep learning systems offer powerful tools for artefact generation, given their
ability to model distributions of data and generate high-fidelity results. In the context of …

Computational creativity composes a collection of activities that are capable of achieving or
simulating behaviors, which can be deemed creative. A frequently articulated criticism for …

We propose a flexible, co-creative framework bringing together multiple machine learning
techniques to assist human users to efficiently produce effective creative designs. We …

Is style reproduction a valid computational creativity task? Does producing output'in the style
of'an existing creator contribute to computational creativity research? Where is the creativity …

Recent advances in machine learning have brought the field closer to computational
creativity research. From a creativity research point of view, this offers the potential to study …

Creativity, a process that generates novel and meaningful ideas, involves increased
association between task-positive (control) and task-negative (default) networks in the …

Traditional wisdom in generative modeling literature is that spurious samples that a model
can generate are errors and they should be avoided. Recent research, however, has shown …

The current tsunami of deep learning has already conquered new areas, such as the
generation of creative content (images, music, text). The motivation is in using the capacity of …

The current tsunami of deep learning has already conquered new areas, such as the
generation of creative content (images, music, text). The motivation is in using the capacity of …