ABSTRACT
Music listening choices are considered to be a factor capable of measuring people's emotions. Thanks to the explosion of streaming music applications in recent years, it is possible to describe listening trends of the global population based on emotional features. In this paper we have analysed the most popular songs from 52 countries on Spotify through their features of danceability, positivity and intensity. This analysis allows exploring how these song features reflect mood trends along with other contextual factors that may affect the population's listening behaviour, such as the weather or the influence of the COVID-19 pandemic. Finally, we have proposed a multivariate time series model to predict the preferred type of music in those countries based on their previous music listening patterns and the contextual factors. The results show some relevant behavioural changes in these patterns due to the effect of the pandemic. Furthermore, the resulting prediction model enables forecasting the type of music listened to in three different groups of countries in the next 4 months with an error around 1%. These results may help to better understand streaming music consumption in businesses related to the music and marketing industry. © 2022 Elsevier B.V.
ABSTRACT
Due to the COVID-19 pandemic, most universities have adapted their learning infrastructure to an increasing demand for online training modalities. However, this type of learning, usually through Learning Management Systems (LMSs), suffer from a lack of direct feedback between students and the educational staff. For that reason, the present work introduces the EMO-learning project, whose key goal is to capture the emotions of students. This is done by means of a deep learning approach, able to timely analyse the face expressions of the students during online lectures. The module has been tested with different students during the academic year 2020-21, showing quite promising results. © 2022 IEEE.