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The influence of listener personality on music choices

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EN
Abstrakty
EN
To deliver better recommendations, music information systems need to go beyond standard methods for the prediction of musical taste. Tracking the listener’s emotions is one way to improve the quality of recommendations. This can be achieved explicitly by asking the listener to report his/her emotional state or implicitly by tracking the context in which the music is heard. However, the factors that induce particular emotions vary among individuals. This paper presents the initial research on the influence of an individual’s personality on his or her choice of music. The psychological profile of a group of 16 students was determined by a questionnaire. The participants were asked to label their own music collections, listen to the music, and mark their emotions using a custom application. Statistical analysis revealed correlations between low-level audio features, personality types, and the emotional states of the students.
Słowa kluczowe
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Strony
163--178
Opis fizyczny
Bibliogr. 32 poz., rys., wykr., tab.
Twórcy
autor
  • Polish-Japanese Academy of Information Technology, Faculty of Information Technology, Department of Multimedia Warsaw, Poland
Bibliografia
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  • [4] Eerola T., Lartillot O., Toiviainen P.: Prediction of Multidimensional Emotional Ratings in Music from Audio Using Multivariate Regression Models. In: Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009) , pp. 621–626, 2009.
  • [5] Furnham A., Strbac L.: Music is as distracting as noise: the differential distraction of background music and noise on the cognitive test performance of introverts and extraverts, Ergonomics , vol. 45(3), pp. 203–217, 2002.
  • [6] Han B., Ho S., Dannenberg R.B., Hwang E.: Smers: Music emotion recognition using support vector regression. In: Proceedings of the 10th International Society for Music Information Retrieval Conference (ISMIR 2009) , pp. 651–656, 2009.
  • [7] Hu X., Downie J.S.: Improving mood classification in music digital libraries by combining lyrics and audio. In: Proceedings of the 10th annual joint conference on Digital libraries , pp. 159–168, ACM, 2010.
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  • [9] Jung C.G.: Psychological types , Routledge, 2014.
  • [10] Juslin P.N., Laukka P.: Expression, perception, and induction of musical emotions: A review and a questionnaire study of everyday listening, Journal of New Music Research , vol. 33(3), pp. 217–238, 2004.
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  • [13] Knight W.E., Rickard N.S.: Relaxing music prevents stress-induced increases in subjective anxiety, systolic blood pressure, and heart rate in healthy males and females, Journal of Music Therapy , vol. 38(4), pp. 254–272, 2001.
  • [14] Lane A.M., Terry P.C.: The nature of mood: Development of a conceptual model with a focus on depression, Journal of Applied Sport Psychology , vol. 12(1), pp. 16–33, 2000.
  • [15] Lartillot O., Toiviainen P., Eerola T.: A Matlab toolbox for music information retrieval. In: Data analysis, machine learning and applications , pp. 261–268, Springer, 2008.
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  • [18] Meyer L.: Emotion and meaning in music , University of Chicago Press, 1956.
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  • [20] North A.C., Hargreaves D.J.: Situational influences on reported musical preference, Psychomusicology: A Journal of Research in Music Cognition , vol. 15(1–2), pp. 30–45, 1996.
  • [21] Oliver N., Kreger-Stickles L.: PAPA: Physiology and Purpose-Aware Automatic Playlist Generation. In: Proceedings of 7th International Conference on Music Information Retrieval , pp. 250–253, 2006.
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  • [25] Rentfrow P.J., Gosling S.D.: The do re mi’s of everyday life: the structure and personality correlates of music preferences, Journal of Personality and Social Psychology , vol. 84(6), pp. 1236–1256, 2003.
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  • [27] Sloboda J.A., Juslin P.N.: At the interface between the inner and outer world. In: Handbook of music and emotion , pp. 73–97, 2010.
  • [28] Soto C.J., John O.P.: Ten facet scales for the Big Five Inventory: Convergence with NEO PI-R facets, self-peer agreement, and discriminant validity, Journal of Research in Personality , vol. 43(1), pp. 84–90, 2009.
  • [29] Strus W., Cieciuch J., Rowiński T.: Circumplex structure of personality traits measured with the IPIP-45AB5C questionnaire in Poland, Personality and Individual Differences , vol. 71, pp. 77–82, 2014.
  • [30] Su J.H., Yeh H.H., Yu P.S., Tseng V.S.: Music recommendation using content and context information mining, Intelligent Systems, IEEE , vol. 25(1), pp. 16–26, 2010.
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  • [32] Wieczorkowska A., Synak P., Lewis R., Ra ́s Z.W.: Extracting emotions from music data, Foundations of Intelligent Systems , pp. 456–465, Springer, 2005
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-949e825b-ac8b-430f-aa23-28ce3666dc10
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