David Daniel Albarracín Molina

David Daniel Albarracín Molina

Creativity, or the ability to produce new useful ideas, is commonly associated to the human being; but there are many other examples in nature where this phenomenon can be observed. Inspired by this fact, in engineering and particularly in computational sciences, many different models have been developed to tackle a number of problems. Composing music, a form of art broadly present along the human history, is the main topic addressed in this thesis. Taking advantage of the kind of ideas that bring diversity and creativity to nature and computation, we present Melomics: an algorithmic composition method based on evolutionary search. The solutions have a genetic encoding based on formal grammars and these are interpreted in a complex developmental process followed by a fitness assessment, to produce valid music compositions in standard formats. The system has exhibited a high creative power and versatility to produce music of different types and it has been tested, proving on many occasions the outcome to be indistinguishable from the music made by human composers. The system has also enabled the emergence of a set of completely novel applications: from effective tools to help anyone to easily obtain the precise music that they need, to radically new uses, such as adaptive music for therapy, exercise, amusement and many others. It seems clear that automated composition is an active research area and that countless new uses will be discovered.

Jose David Fernández Rodríguez, David Daniel Albarracín Molina, Jesús Hormigo Cebolla

Oftentimes TV producers of motor-racing programs overlay visual and textual media to provide on-screen context about drivers, such as a driver's name, position or photo. Typically this is accomplished by a human producer who visually identifies the drivers on screen, manually toggling the contextual media associated to each one and coordinating with cameramen and other TV producers to keep the racer in the shot while the contextual media is on screen. This labor-intensive and highly dedicated process is mostly suited to static overlays and makes it difficult to overlay contextual information about many drivers at the same time in short shots. This paper presents a system that largely automates these tasks and enables dynamic overlays using deep learning to track the drivers as they move on screen, without human intervention. This system is not merely theoretical, but an implementation has already been deployed during live races by a TV production company at Formula E races. We present the challenges faced during the implementation and discuss the implications. Additionally, we cover future applications and roadmap of this new technological development.