Estudio de la recepción en movimiento de la televisión digital utilizando LDM.

[EN]The purpose of this project is to study and analyse signals of digital television, provided for mobile services, by using LDM technology. In order to achieve this, it will be needed the use of different transmission configurations (modulation, code rate…) suitable for this type of service, as well as propagation channels showing movement situations, such as pedestrian or mobile. This project comes as a response of recent research in LDM technology, which has been proposed as a Physical Layer technology to the ATSC 3.0 Next Generation Digital TV standard.

User Interface Abstraction for enabling TV set based Inclusive Access to the Information Society

199 p.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

Study of emotions in human-computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested.