This paper reports on three demonstrators developed to provide personalized, enhanced experiences of classical music, within the EU FP7 project Performances as Highly Enriched and Interactive Concert experiences (PHENICX)1: (i) an interface for accessing supplemental multimodal material about music items, (ii) a recommender system for visualizations of classical music, and (iii) a recommender sys- Tem for tagging. The personalization in all three demos is achieved through modeling users' personality and musical sophistication. The links to the web interfaces are provided as well as the outcomes of quantitative and qualitative evaluations.@en

In this position paper we present the scenario of making interventions for increasing the classical music concert-going behaviour of end users. Within the FP7 Phenicx project we are developing a personalized persuasive system that attempts at changing the concert-going behaviour of users. The system is based on the theory of planned behaviour user model for predicting whether a user will attend a concert or not. Our goal is to develop a machine learning algorithm that will extract the user model parameters unobtrusively from the micro-blogs of the users. We plan to perform a user study to build the training dataset and to test the system on real users within the project.

@en

MusiClef was one of the "brave new tasks" at MediaEval 2013 with a multimodal approach that combined music, video and textual information in order to evaluate systems that recommend a music soundtrack given the video of a commercial and the information on the product to be advertised.

@en

This paper presents the MusiClef data set, a multimodal data set of professionally annotated music. It includes editorial metadata about songs, albums, and artists, as well as MusicBrainz identifiers to facilitate linking to other data sets. In addition, several state-of-the-art audio features are provided. Different sets of annotations and music context data - collaboratively generated user tags, web pages about artists and albums, and the annotation labels provided by music experts - are included too. Versions of this data set were used in the MusiClef evaluation campaigns in 2011 and 2012 for auto-tagging tasks. We report on the motivation for the data set, on its composition, on related sets, and on the evaluation campaigns in which versions of the set were already used. These campaigns likewise represent one use case, i.e. music auto-tagging, of the data set. The complete data set is publicly available for download at http://www.cp.jku.at/musiclef.

@en

MusiClef is a multimodal music benchmarking initiative that will be running a MediaEval 2012 Brave New Task on Multimodal Music Tagging. This paper describes the setup of this task, showing how it complements existing benchmarking initiatives and fosters less explored methodological directions in Music Information Retrieval. MusiClef deals with a concrete use case, encourages multimodal approaches based on these, and strives for transparency of results as much as possible. Transparency is encouraged at several levels and stages, from the feature extraction procedure up to the evaluation phase, in which a dedicated categorization of ground truth tags will be used to deepen the understanding of the relation between the proposed approaches and experimental results.

@en

In this paper, we give an overview of the MusiClef Brave New Task at MediaEval 2012. After introducing the background of the task and related initiatives in the music information retrieval domain, we give an overview of the actual task and baseline results obtained for a reference implementation by the task organizers.

@en