Sound source localization using a two-microphone array is an active area of research, with considerable potential for use with video conferencing, mobile devices, and robotics. Based on the observed time-differences of arrival between sound signals, a probability distribution of the location of the sources is considered to estimate the actual source positions. However, these algorithms assume a given number of sound sources. This paper describes an updated research account on the solution presented in Escolano et al. [J. Acoust. Am. Soc. 132(3), 1257–1260 (2012)], where nested sampling is used to explore a probability distribution of the source position using a Laplacian mixture model, which allows both the number and position of speech sources to be inferred. This paper presents different experimental setups and scenarios to demonstrate the viability of the proposed method, which is compared with some of the most popular sampling methods, demonstrating that nested sampling is an accurate tool for speech localization.

One of the main issues within the field of social robotics is to endow robots with the ability to direct attention to people with whom they are interacting. Different approaches follow bio-inspired mechanisms, merging audio and visual cues to localize a person using multiple sensors. However, most of these fusion mechanisms have been used in fixed systems, such as those used in video-conference rooms, and thus, they may incur difficulties when constrained to the sensors with which a robot can be equipped. Besides, within the scope of interactive autonomous robots, there is a lack in terms of evaluating the benefits of audio-visual attention mechanisms, compared to only audio or visual approaches, in real scenarios. Most of the tests conducted have been within controlled environments, at short distances and/or with off-line performance measurements. With the goal of demonstrating the benefit of fusing sensory information with a Bayes inference for interactive robotics, this paper presents a system for localizing a person by processing visual and audio data. Moreover, the performance of this system is evaluated and compared via considering the technical limitations of unimodal systems. The experiments show the promise of the proposed approach for the proactive detection and tracking of speakers in a human-robot interactive framework.

Higher education in Spain has to deal with constant troubles and uncertainties due to the economic crisis, high rates of unemployment in young people, lack for study habits in secondary school and legal fluctuations. This uncertain environment does not foster student effort and it is behind the important rates of abandon in higher education. The Bologna Process was thought to create a new paradigm in higher education in the European Union. However, the changes came from the top (governments) to the bottom (lectures and students) so they were not properly supported by specialized training oriented to lecturers. It did not include the appropriate changes in lower education stages (secondary education) to prepare student when facing University. Therefore, in the past decade several new teaching methodologies have appeared to deal with student demotivation and to fight against dropouts. Those methodologies try to keep the students engaged during the whole course paying more attention to their learning process, attitudes, motivations and expectations. Consequently, in this paper, we present a four-year experiment whose main objective is to keep students engaged during the whole year and to foster their motivation in order to increase their learning outcomes. The experiment is based on the application of gamification to the assessment process emulating a traditional platform video-game, like Super Mario. The results show that this experiment was positive for most students who achieved good marks and good rates of task completion.