%0 Conference Proceedings %B Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016) %D 2016 %T An Analysis of Agreement in Classical Music Perception and Its Relationship to Listener Characteristics %A Markus Schedl %A Hamid Eghbal-zadeh %A Emilia Gómez %A Marko Tkalčič %B Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR 2016) %C New York, USA %8 08/2016 %G eng %0 Conference Proceedings %B CHI - Human Factors in Computing Systems %D 2016 %T Machine Learning of Personal Gesture Variation in Music Conducting %A Sarasua, Alvaro %A Caramiaux, Baptiste %A Tanaka, Atau %X

This note presents a system that learns expressive and idiosyncratic gesture variations for gesture-based interaction. The system is used as an interaction technique in a music conducting scenario where gesture variations drive music articulation. A simple model based on Gaussian Mixture Modeling is used to allow the user to configure the system by providing variation examples. The system performance and the influence of user musical expertise is evaluated in a user study, which shows that the model is able to learn idiosyncratic variations that allow users to control articulation, with better performance for users with musical expertise.

%B CHI - Human Factors in Computing Systems %I ACM Press %C San Jose, CA %P 3428-3432 %G eng %R 10.1145/2858036.2858328 %0 Conference Paper %B Proceedings of the 7th ACM Multimedia Systems Conference (MMSys) %D 2016 %T A Personality-based Adaptive System for Visualizing Classical Music Performances %A Markus Schedl %A Mark Melenhorst %A Cynthia C.S. Liem %A Agustín Martorell %A Óscar Mayor %A Marko Tkalčič %B Proceedings of the 7th ACM Multimedia Systems Conference (MMSys) %C Klagenfurt, Austria %8 May %G eng %0 Conference Paper %B Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR) %D 2016 %T Personalized Retrieval and Browsing of Classical Music and Supporting Multimedia Material %A Marko Tkalčič %A Markus Schedl %A Cynthia C.S. Liem %A Mark Melenhorst %B Proceedings of the ACM International Conference on Multimedia Retrieval (ICMR) %C New York, USA %8 June %G eng %0 Conference Paper %B Proceedings of the 22nd International Conference on MultiMedia Modeling (MMM 2016) %D 2016 %T Using Instagram Picture Features to Predict Users' Personality %A Ferwerda, Bruce %A Schedl, Markus %A Tkalčič, Marko %B Proceedings of the 22nd International Conference on MultiMedia Modeling (MMM 2016) %C Miami, USA %8 January %G eng %0 Conference Paper %B Ninth Triennial Conference of the European Society for the Cognitive Sciences of Music (ESCOM) %D 2015 %T Correlations Between Musical Descriptors and Emotions Recognized in Beethoven’s Eroica %A Erika S. Trent %A Emilia Gómez %K classical music %K emotion %K music description %K music information retrieval %K personalization %X

Investigations on music and emotion have identified broad musical elements that influence emotions recognized by listeners, such as timbre, rhythm, melody, and harmony. Not many studies have studied the correlation between quantifiable musical descriptors and their associated emotions; furthermore, only few studies have focused on how listeners’ demographic and musical backgrounds influence the emotion they recognize. In this preliminary study, participants rated how strongly they recognized the six GEMS emotions (transcendence, peacefulness, power, joyful activation, tension, and sadness) while listening to excerpts from Beethoven’s Eroica. Musical descriptors (loudness, brightness, noisiness, tempo/rhythm, harmony, and timbre) were also extracted from each excerpt. Results indicate significant correlations between emotional ratings and musical descriptors, notably positive correlations between key clarity and peacefulness/joyful activation ratings, and negative correlations between key clarity and tension/sadness ratings. Key clarity refers to the key strength associated to the best key candidate; as such, these results suggest that listeners recognize positive emotions in music with a straightforward key, whereas listeners recognize negative emotions in music with a less clear sense of key. The second part of the study computed correlations between demographics and emotional ratings, to determine whether people of similar demographic and musical backgrounds recognized similar emotions. The results indicate that na{\"ıve listeners (i.e. younger subjects, and subjects with less frequent exposure to classical music) experienced more similar emotions from the same musical excerpts than did other subjects. Our findings contribute to developing a quantitative understanding of how musical descriptors, and listeners’ backgrounds, correlate with emotions recognized by listeners.

%B Ninth Triennial Conference of the European Society for the Cognitive Sciences of Music (ESCOM) %C Manchester, UK %8 17/08/2015 %G eng %U http://phenicx.upf.edu/system/files/publications/0168TrentGomez-ESCOM2015.pdf %0 Conference Paper %B Proceedings of the 37th European Conference on Information Retrieval (ECIR 2015) %D 2015 %T On the Influence of User Characteristics on Music Recommendation %A Markus Schedl %A David Hauger %A Katayoun Farrahi %A Marko Tkalčič %B Proceedings of the 37th European Conference on Information Retrieval (ECIR 2015) %C Vienna, Austria %8 March–April %G eng %0 Journal Article %J UMAP 2015, Springer LNCS 9146 %D 2015 %T Personality Correlates for Digital Concert Program Notes %A Tkalčič, Marko %A Ferwerda, Bruce %A Hauger, David %A Schedl, Markus %K classical music %K digital program notes %K personality %B UMAP 2015, Springer LNCS 9146 %G eng %R 10.1007/978-3-319-20267-9 32 %0 Conference Paper %B Extended Proceedings of the 22nd International Conference on User Modeling, Adaptation and Personalization (UMAP 2015) %D 2015 %T Personality & Emotional States: Understanding Users’ Music Listening Needs %A Ferwerda, Bruce %A Schedl, Markus %A Tkalčič, Marko %B Extended Proceedings of the 22nd International Conference on User Modeling, Adaptation and Personalization (UMAP 2015) %C Dublin, Ireland %8 June–July %G eng %0 Conference Paper %B Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA '15 %D 2015 %T Personality Traits Predict Music Taxonomy Preferences %A Ferwerda, Bruce %A Yang, Emily %A Schedl, Markus %A Tkalčič, Marko %B Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems - CHI EA '15 %@ 9781450331463 %G eng %U http://dx.doi.org/10.1145/2702613.2732754 http://dl.acm.org/citation.cfm?doid=2702613.2732754 %R 10.1145/2702613.2732754 %0 Conference Paper %B Proceedings of the 1st ACM International Workshop on Internet-Scale Multimedia Management (ISMM 2014) %D 2014 %T Genre-based Analysis of Social Media Data on Music Listening Behavior %A Markus Schedl %A Marko Tkalčič %B Proceedings of the 1st ACM International Workshop on Internet-Scale Multimedia Management (ISMM 2014) %C Orlando, FL, USA %8 November %G eng %0 Journal Article %J Springer Multimedia Tools and Applications %D 2014 %T The impact of hesitation, a social signal, on a user’s quality of experience in multimedia content retrieval %A Vodlan, Tomaż %A Tkalčič, Marko %A Košir, Andrej %K computer interaction %K hesitation %K human %K social signals %K video-on-demand %B Springer Multimedia Tools and Applications %G eng %U http://link.springer.com/10.1007/s11042-014-1933-2 %R 10.1007/s11042-014-1933-2 %0 Conference Paper %B Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR 2014) %D 2014 %T Impact of Listening Behavior on Music Recommendation %A Katayoun Farrahi %A Markus Schedl %A Andreu Vall %A David Hauger %A Marko Tkalčič %B Proceedings of the 15th International Society for Music Information Retrieval Conference (ISMIR 2014) %C Taipei, Taiwan %8 October %G eng %0 Conference Paper %B Proceedings of the 6th ASE International Conference on Social Computing (SocialCom 2014) %D 2014 %T To Post or Not to Post: The Effects of Persuasive Cues and Group Targeting Mechanisms on Posting Behavior %A Bruce Ferwerda %A Markus Schedl %A Marko Tkalčič %B Proceedings of the 6th ASE International Conference on Social Computing (SocialCom 2014) %C Stanford, USA %8 May %G eng %0 Conference Paper %B Proceedings of the 2nd Workshop on Emotions and Personality in Personalized Services (EMPIRE 2014) %D 2014 %T Using Social Media Mining for Estimating Theory of Planned Behaviour Parameters %A Marko Tkalčič %A Bruce Ferwerda %A Markus Schedl %A Cynthia C. S. Liem %A Mark S. Melenhorst %A Ante Odić %A Andrej Košir %B Proceedings of the 2nd Workshop on Emotions and Personality in Personalized Services (EMPIRE 2014) %C Aalborg, Denmark %8 July %G eng %0 Conference Paper %B Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation %D 2013 %T How to Improve the Statistical Power of the 10-fold Cross Validation Scheme in Recommender Systems %A Košir, Andrej %A Odić, Ante %A Tkalčič, Marko %K evaluation %K experimental design %K folding %K paired testing %K recommender systems %B Proceedings of the International Workshop on Reproducibility and Replication in Recommender Systems Evaluation %I ACM %C New York, NY, USA %@ 978-1-4503-2465-6 %G eng %U http://doi.acm.org/10.1145/2532508.2532510 %R 10.1145/2532508.2532510 %0 Conference Paper %B Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013) %D 2013 %T The Million Musical Tweets Dataset: What Can We Learn From Microblogs %A David Hauger %A Markus Schedl %A Andrej Košir %A Marko Tkalčič %B Proceedings of the 14th International Society for Music Information Retrieval Conference (ISMIR 2013) %C Curitiba, Brazil %8 November %G eng %0 Conference Paper %B EMPIRE 2013: Emotions and Personality in Personalized Services %D 2013 %T Personality and Social Context: Impact on Emotion Induction from Movies %A Ante Odić %A Marko Tkalčič %A Jurij F. Tasič %A Andrej Košir %X
In this paper we describe our preliminary work on understanding the impact of personality on the emotion induction in di erent social circumstances during the consumption of movies, for the purposeof the context-aware recommender system for movies. The purpose ofthis study is to answer two research questions: is there a di erence in emotion induction when users are alone as opposed to when they are with company during watching the movie, and do di erent personality pro les in uence the emotion induction when users are alone as opposed to when they are with company during watching the movie? We have used the (LDOS-CoMoDa) dataset which contains ratings and associated contextual information for the consumed movies, as well as Big Five personality pro les of the users. The results showed that there is an in uence of social context on emotion induction, and that personality factors have to be taken into consideration since for the di erent groups of users, based on the personality factors, the emotion induction was in uenced di erently
%B EMPIRE 2013: Emotions and Personality in Personalized Services %I http://ceur-ws.org/Vol-997/#empire2013 %C Rome, Italy %8 06/2013 %G eng %U http://ceur-ws.org/Vol-997/empire2013_paper_5.pdf