00498nas a2200133 4500008004100000245010800041210006900149260002700218100001900245700002400264700001900288700002100307856003600328 2016 eng d00aAn Analysis of Agreement in Classical Music Perception and Its Relationship to Listener Characteristics0 aAnalysis of Agreement in Classical Music Perception and Its Rela aNew York, USAc08/20161 aSchedl, Markus1 aEghbal-zadeh, Hamid1 aGómez, Emilia1 aTkalčič, Marko uhttp://phenicx.upf.edu/node/27801156nas a2200157 4500008004100000022001800041245007100059210006900130260002800199300001400227520066000241100002000901700002400921700001700945856003600962 2016 eng d a978145033362700aMachine Learning of Personal Gesture Variation in Music Conducting0 aMachine Learning of Personal Gesture Variation in Music Conducti aSan Jose, CAbACM Press a3428-34323 a
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.
1 aSarasua, Alvaro1 aCaramiaux, Baptiste1 aTanaka, Atau uhttp://phenicx.upf.edu/node/27700545nas a2200157 4500008004100000245008500041210006900126260002900195100001900224700002100243700002400264700002400288700001800312700002100330856003600351 2016 eng d00aA Personality-based Adaptive System for Visualizing Classical Music Performances0 aPersonalitybased Adaptive System for Visualizing Classical Music aKlagenfurt, AustriacMay1 aSchedl, Markus1 aMelenhorst, Mark1 aLiem, Cynthia, C.S.1 aMartorell, Agustín1 aMayor, Óscar1 aTkalčič, Marko uhttp://phenicx.upf.edu/node/27300483nas a2200133 4500008004100000245009400041210006900135260002400204100002100228700001900249700002400268700002100292856003600313 2016 eng d00aPersonalized Retrieval and Browsing of Classical Music and Supporting Multimedia Material0 aPersonalized Retrieval and Browsing of Classical Music and Suppo aNew York, USAcJune1 aTkalčič, Marko1 aSchedl, Markus1 aLiem, Cynthia, C.S.1 aMelenhorst, Mark uhttp://phenicx.upf.edu/node/27400416nas a2200121 4500008004100000245006700041210006600108260002400174100002000198700001900218700002100237856003600258 2016 eng d00aUsing Instagram Picture Features to Predict Users' Personality0 aUsing Instagram Picture Features to Predict Users Personality aMiami, USAcJanuary1 aFerwerda, Bruce1 aSchedl, Markus1 aTkalčič, Marko uhttp://phenicx.upf.edu/node/25202570nas a2200181 4500008004100000245009300041210006900134260003100203520192600234653002002160653001202180653002202192653003202214653002002246100002102266700001902287856008202306 2015 eng d00aCorrelations Between Musical Descriptors and Emotions Recognized in Beethoven’s Eroica0 aCorrelations Between Musical Descriptors and Emotions Recognized aManchester, UKc17/08/20153 aInvestigations 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.
10aclassical music10aemotion10amusic description10amusic information retrieval10apersonalization1 aTrent, Erika, S.1 aGómez, Emilia uhttp://phenicx.upf.edu/system/files/publications/0168TrentGomez-ESCOM2015.pdf00457nas a2200133 4500008004100000245006900041210006200110260003500172100001900207700001800226700002200244700002100266856003600287 2015 eng d00aOn the Influence of User Characteristics on Music Recommendation0 aInfluence of User Characteristics on Music Recommendation aVienna, AustriacMarch–April1 aSchedl, Markus1 aHauger, David1 aFarrahi, Katayoun1 aTkalčič, Marko uhttp://phenicx.upf.edu/node/17700497nas a2200157 4500008004100000245006100041210006100102653002000163653002600183653001600209100002100225700002000246700001800266700001900284856003600303 2015 eng d00aPersonality Correlates for Digital Concert Program Notes0 aPersonality Correlates for Digital Concert Program Notes10aclassical music10adigital program notes10apersonality1 aTkalčič, Marko1 aFerwerda, Bruce1 aHauger, David1 aSchedl, Markus uhttp://phenicx.upf.edu/node/22600442nas a2200121 4500008004100000245008100041210006900122260003300191100002000224700001900244700002100263856003600284 2015 eng d00aPersonality & Emotional States: Understanding Users’ Music Listening Needs0 aPersonality Emotional States Understanding Users Music Listening aDublin, IrelandcJune–July1 aFerwerda, Bruce1 aSchedl, Markus1 aTkalčič, Marko uhttp://phenicx.upf.edu/node/26200483nas a2200133 4500008004100000020001800041245005800059210005800117100002000175700001600195700001900211700002100230856009800251 2015 eng d a978145033146300aPersonality Traits Predict Music Taxonomy Preferences0 aPersonality Traits Predict Music Taxonomy Preferences1 aFerwerda, Bruce1 aYang, Emily1 aSchedl, Markus1 aTkalčič, Marko uhttp://dx.doi.org/10.1145/2702613.2732754 http://dl.acm.org/citation.cfm?doid=2702613.273275400401nas a2200109 4500008004100000245007400041210006900115260003100184100001900215700002100234856003600255 2014 eng d00aGenre-based Analysis of Social Media Data on Music Listening Behavior0 aGenrebased Analysis of Social Media Data on Music Listening Beha aOrlando, FL, USAcNovember1 aSchedl, Markus1 aTkalčič, Marko uhttp://phenicx.upf.edu/node/17500624nas a2200181 4500008004100000022001400041245011500055210006900170653002500239653001500264653001000279653001900289653002000308100001900328700002100347700001900368856005500387 2014 eng d a1380-750100aThe impact of hesitation, a social signal, on a user’s quality of experience in multimedia content retrieval0 aimpact of hesitation a social signal on a user s quality of expe10acomputer interaction10ahesitation10ahuman10asocial signals10avideo-on-demand1 aVodlan, Tomaż1 aTkalčič, Marko1 aKošir, Andrej uhttp://link.springer.com/10.1007/s11042-014-1933-200462nas a2200145 4500008004100000245005700041210005700098260002800155100002200183700001900205700001700224700001800241700002100259856003600280 2014 eng d00aImpact of Listening Behavior on Music Recommendation0 aImpact of Listening Behavior on Music Recommendation aTaipei, TaiwancOctober1 aFarrahi, Katayoun1 aSchedl, Markus1 aVall, Andreu1 aHauger, David1 aTkalčič, Marko uhttp://phenicx.upf.edu/node/18400461nas a2200121 4500008004100000245011000041210006900151260002300220100002000243700001900263700002100282856003600303 2014 eng d00aTo Post or Not to Post: The Effects of Persuasive Cues and Group Targeting Mechanisms on Posting Behavior0 aTo Post or Not to Post The Effects of Persuasive Cues and Group aStanford, USAcMay1 aFerwerda, Bruce1 aSchedl, Markus1 aTkalčič, Marko uhttp://phenicx.upf.edu/node/17900572nas a2200169 4500008004100000245008400041210006900125260002700194100002100221700002000242700001900262700002500281700002500306700001600331700001900347856003600366 2014 eng d00aUsing Social Media Mining for Estimating Theory of Planned Behaviour Parameters0 aUsing Social Media Mining for Estimating Theory of Planned Behav aAalborg, DenmarkcJuly1 aTkalčič, Marko1 aFerwerda, Bruce1 aSchedl, Markus1 aLiem, Cynthia, C. S.1 aMelenhorst, Mark, S.1 aOdić, Ante1 aKošir, Andrej uhttp://phenicx.upf.edu/node/18000653nas a2200193 4500008004100000020002200041245010300063210006900166260002700235653001500262653002400277653001200301653001900313653002400332100001900356700001600375700002100391856004700412 2013 eng d a978-1-4503-2465-600aHow to Improve the Statistical Power of the 10-fold Cross Validation Scheme in Recommender Systems0 aHow to Improve the Statistical Power of the 10fold Cross Validat aNew York, NY, USAbACM10aevaluation10aexperimental design10afolding10apaired testing10arecommender systems1 aKošir, Andrej1 aOdić, Ante1 aTkalčič, Marko uhttp://doi.acm.org/10.1145/2532508.253251000462nas a2200133 4500008004100000245007400041210006900115260003100184100001800215700001900233700001900252700002100271856003600292 2013 eng d00aThe Million Musical Tweets Dataset: What Can We Learn From Microblogs0 aMillion Musical Tweets Dataset What Can We Learn From Microblogs aCuritiba, BrazilcNovember1 aHauger, David1 aSchedl, Markus1 aKošir, Andrej1 aTkalčič, Marko uhttp://phenicx.upf.edu/node/13901890nas a2200157 4500008004100000024005400041245007600095210006900171260006500240520129500305100001601600700002101616700002201637700001901659856005401678 2013 eng d ahttp://ceur-ws.org/Vol-997/empire2013_paper_5.pdf00aPersonality and Social Context: Impact on Emotion Induction from Movies0 aPersonality and Social Context Impact on Emotion Induction from aRome, Italybhttp://ceur-ws.org/Vol-997/#empire2013c06/20133 a