2 research outputs found
Low-cost methodologies and devices applied to measure, model and self-regulate emotions for Human-Computer Interaction
En aquesta tesi s'exploren les diferents metodologies d'anà lisi de l'experiència UX des d'una visió centrada en usuari. Aquestes metodologies clà ssiques i fonamentades només permeten extreure dades cognitives, és a dir les dades que l'usuari és capaç de comunicar de manera conscient. L'objectiu de la tesi és proposar un model basat en l'extracció de dades biomètriques per complementar amb dades emotives (i formals) la informació cognitiva abans esmentada. Aquesta tesi no és només teòrica, ja que juntament amb el model proposat (i la seva evolució) es mostren les diferents proves, validacions i investigacions en què s'han aplicat, sovint en conjunt amb grups de recerca d'altres à rees amb èxit.En esta tesis se exploran las diferentes metodologÃas de análisis de la experiencia UX desde una visión centrada en usuario. Estas metodologÃas clásicas y fundamentadas solamente permiten extraer datos cognitivos, es decir los datos que el usuario es capaz de comunicar de manera consciente. El objetivo de la tesis es proponer un modelo basado en la extracción de datos biométricos para complementar con datos emotivos (y formales) la información cognitiva antes mencionada.
Esta tesis no es solamente teórica, ya que junto con el modelo propuesto (y su evolución) se muestran las diferentes pruebas, validaciones e investigaciones en la que se han aplicado, a menudo en conjunto con grupos de investigación de otras áreas con éxito.In this thesis, the different methodologies for analyzing the UX experience are explored from a user-centered perspective. These classical and well-founded methodologies only allow the extraction of cognitive data, that is, the data that the user is capable of consciously communicating. The objective of this thesis is to propose a methodology that uses the extraction of biometric data to complement the aforementioned cognitive information with emotional (and formal) data.
This thesis is not only theoretical, since the proposed model (and its evolution) is complemented with the different tests, validations and investigations in which they have been applied, often in conjunction with research groups from other areas with success
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Tension-driven Automatic Music Generation
The Ancient Greeks are one of the first civilisations we know of to have created algorithms to compose music. Since then, algorithmic techniques have vastly improved with increasingly sophisticated computers. In the last two decades, much research in this area has focused on two goals: designing algorithms which generate music as close as possible to that of human composers and implementing those algorithms to automatically generate music in interactive scenarios, such as video games.
To meet these goals, automatically generated music should:
- focus on higher-level concepts, such as musical tension,
- have long-term structure, and
- be able to adapt to changes in real time.
Combining these three requirements is, however, a challenging task. This dissertation investigates three steps to overcome this challenge. First, we argue that Lerdahl’s model of musical tension is suited to the automatic generation of tonal music that has long-term structure and that matches a given tension profile. By means of an illustrative example, we review Lerdhal’s model and implement a novel computational system to automate it. Second, we show that an effective generation strategy is to combine statistical methods with both rule-based methods and generative grammars to create a music generation system. Third, we implement the system and evaluate it through a collection of computational tests and empirical studies.
Our evaluation shows that:
(1) the system works effectively in real time, as long as the input tension profiles do not contain too many steep transitions,
(2) the hierarchical structure perceived by listeners matches the patterns intended by the system in the generated music, and
(3) tension-changing input profiles are accurately matched by the generated music