Information-theoretic measures of music listening behaviour

We present an information-theoretic approach to the mea- surement of users’ music listening behaviour and selection of music features. Existing ethnographic studies of mu- sic use have guided the design of music retrieval systems however are typically qualitative and exploratory in nature. We introduce the SPUD dataset, comprising 10, 000 hand- made playlists, with user and audio stream metadata. With this, we illustrate the use of entropy for analysing music listening behaviour, e.g. identifying when a user changed music retrieval system. We then develop an approach to identifying music features that reflect users’ criteria for playlist curation, rejecting features that are independent of user behaviour. The dataset and the code used to produce it are made available. The techniques described support a quantitative yet user-centred approach to the evaluation of music features and retrieval systems, without assuming objective ground truth labels

Information-theoretic measures of music listening behaviour

We present an information-theoretic approach to the mea- surement of users’ music listening behaviour and selection of music features. Existing ethnographic studies of mu- sic use have guided the design of music retrieval systems however are typically qualitative and exploratory in nature. We introduce the SPUD dataset, comprising 10, 000 hand- made playlists, with user and audio stream metadata. With this, we illustrate the use of entropy for analysing music listening behaviour, e.g. identifying when a user changed music retrieval system. We then develop an approach to identifying music features that reflect users’ criteria for playlist curation, rejecting features that are independent of user behaviour. The dataset and the code used to produce it are made available. The techniques described support a quantitative yet user-centred approach to the evaluation of music features and retrieval systems, without assuming objective ground truth labels

A Dose of Reality: Overcoming Usability Challenges in VR Head-Mounted Displays

We identify usability challenges facing consumers adopting Virtual Reality (VR) head-mounted displays (HMDs) in a survey of 108 VR HMD users. Users reported significant issues in interacting with, and being aware of their real-world context when using a HMD. Building upon existing work on blending real and virtual environments, we performed three design studies to address these usability concerns. In a typing study, we show that augmenting VR with a view of reality significantly corrected the performance impairment of typing in VR. We then investigated how much reality should be incorporated and when, so as to preserve users’ sense of presence in VR. For interaction with objects and peripherals, we found that selectively presenting reality as users engaged with it was optimal in terms of performance and users’ sense of presence. Finally, we investigated how this selective, engagement-dependent approach could be applied in social environments, to support the user’s awareness of the proximity and presence of others

Design and Evaluation of a Probabilistic Music Projection Interface

We describe the design and evaluation of a probabilistic interface for music exploration and casual playlist generation. Predicted subjective features, such as mood and genre, inferred from low-level audio features create a 34- dimensional feature space. We use a nonlinear dimensionality reduction algorithm to create 2D music maps of tracks, and augment these with visualisations of probabilistic mappings of selected features and their uncertainty. We evaluated the system in a longitudinal trial in users’ homes over several weeks. Users said they had fun with the interface and liked the casual nature of the playlist generation. Users preferred to generate playlists from a local neighbourhood of the map, rather than from a trajectory, using neighbourhood selection more than three times more often than path selection. Probabilistic highlighting of subjective features led to more focused exploration in mouse activity logs, and 6 of 8 users said they preferred the probabilistic highlighting mode

Engaging with music retrieval

Music collections available to listeners have grown at a dramatic pace, now spanning tens of millions of tracks. Interacting with a music retrieval system can thus be overwhelming, with users offered ‘too-much-choice’. The level of engagement required for such retrieval interactions can be inappropriate, such as in mobile or multitasking contexts. Music recommender systems are widely employed to address this issue, however tend toward the opposite extreme of disempowering users and suffer from issues of subjectivity and confounds, such as the equalisation of tracks. This challenge and the styles of retrieval interaction involved are characterised in terms of user engagement in music retrieval, and the relationships between existing conceptualisations of user engagement is explored. Using listening histories and work from music psychology, a set of engagement-stratified profiles of listening behaviour are developed. A dataset comprising the playlists of thousands of users is used to contribute a user-centric approach to feature selection. The challenge of designing music retrieval for different levels of user engagement is first explored with a proof of concept, low engagement music retrieval system enabling users to casually retrieve music by tapping its rhythm as a query. The design methodology is then generalised with an engagement-dependent system, allowing users to denote their level of engagement and thus the specificity of their music queries. The engagement-dependent retrieval interaction is then explored as a component in a commercial music system. This thesis contributes the engagement-stratified profiles and metrics of listening behaviour, a corresponding design methodology for interaction, and presents a set of research and commercial applications for music retrieval

D4.3 Overview report on relevant socio-economic situation in EU Member States. Dataset on economic situation as input in EMT and for other WPs

This report gives an overview of relevant EU Member States' economic and social situation, compiling indicators that can affect migration decisions, planned destinations, and integration trajectories after arrival. All data used in the report originates from the Eurostat database. We identified the database as the most suitable source of live data as it is regularly updated and presents the data in a standardized form that makes it comparable across countries. Furthermore, the Eurostat database provides wide geographic coverage and an extensive set of variables that cover the key economic and social indicators relevant to the context of migration decision-making and attitudes towards migration

A Digital Library for Research Data and Related Information in the Social Sciences

In the social sciences, researchers search for information on the Web, but this is most often distributed on different websites, search portals, digital libraries, data archives, and databases. In this work, we present an integrated search system for social science information that allows finding information around research data in a single digital library. Users can search for research data sets, publications, survey variables, questions from questionnaires, survey instruments, and tools. Information items are linked to each other so that users can see, for example, which publications contain data citations to research data. The integration and linking of different kinds of information increase their visibility so that it is easier for researchers to find information for re-use. In a log-based usage study, we found that users search across different information types, that search sessions contain a high rate of positive signals and that link information is often explored