Abstract
Music and mood carry a strong relationship, which is employed very effectively by classical music, Hollywood’s soundtracks, and pop bands. Affective computing can provide support in selecting music that fits to a given mood. We describe a system that addresses a full range of functionality. It allows the user to semi-automatically tag music with mood descriptions, determines mood from sensors or from the state of a computer game, and plays appropriate music.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Healey, J., Picard, R., Dabek, F.: A new affect-perceiving interface and its application to personalized music selection. In: Proc. of PUI 1998 (1998)
Dornbush, S., Fisher, K., McKay, K., Prikhodko, A., Segall, Z.: Xpod – a human activity and emotion aware music player. In: Proc. of the International Conference on Mobile Technology, Applications and Systems, pp. 1–6 (2005)
Chung, J.-W., Vercoe, G.S.: The affective remixer: personalized music arranging. In: CHI 2006 Extended Abstracts, pp. 393–398 (2006)
Meyers, O.: mySoundTrack: A commonsense playlist generator (2005), http://web.media.mit.edu/~meyers/mysoundtrack.html
Wijnalda, G., Pauws, S., Vignoli, F., Stuckenschmidt, H.: A personalized music system for motivation in sport performance. IEEE Pervasive Computing 04(3), 26–32 (2005)
Oliver, N., Flores-Mangas, F.: MPTrain: a mobile, music and physiology-based personal trainer. In: Proc. of MobileHCI 2006, pp. 21–28 (2006)
Reddy, S., Mascia, J.: Lifetrak: Music in tune with your life. In: Proc. of HCM 2006, pp. 25–34 (2006)
Corthaut, N., Govaerts, S., Duval, E.: Moody tunes: The Rockanango project. In: Proc. of ISMIR 2006, pp. 308–313 (2006)
Livingstone, S.R., Brown, A.R.: Dynamic response: real-time adaptation for music emotion. In: Proc. of IE 2005, pp. 105–111 (2005)
Yang, Y.-H., Liu, C.-C., Chen, H.H.: Music emotion classification: a fuzzy approach. In: Proc. of MULTIMEDIA 2006, pp. 81–84 (2006)
Mandel, M.I., Poliner, G.E., Ellis, D.P.W.: Support vector machine active learning for music retrieval. Multimedia Systems 12(1), 3–13 (2006)
Knees, P., Pohle, T., Schedl, M., Widmer, G.: Combining audio-based similarity with web-based data to accelerate automatic music playlist generation. In: Proc. of MIR 2006, pp. 147–154 (2006)
Andric, A., Xech, P.L., Fantasia, A.: Music mood wheel: Improving browsing experience on digital content through an audio interface. In: Proc. of AXMEDIS 2006, pp. 251–257 (2006)
Ortony, A., Turner, T.J.: What’s basic about basic emotions? Psychological Review 97, 315–331 (1990)
Kalinnen, K.: Emotional ratings of music excerpts in the Western art music repertoire and their self-organization in the Kohonen neural network. Psychology of Music 33(4), 373–379 (2005)
Schubert, E.: Measuring emotion continuously: validity and reliability of the two-dimensional emotion-space. Australian J. of Psychology 51(3), 154–156 (1999)
Mehrabian, A.: Pleasure–arousal–dominance: A general framework for describing and measuring individual differences in temperament. Current Psychology: Developmental, Learning, Personality, Social 14, 261–292 (1996)
Li, T., Ogihara, M.: Detecting emotion in music. In: Proc. of ISMIR 2003, pp. 239–240 (2003)
Pampalk, E., Dixon, S., Widmer, G.: On the evaluation of perceptual similarity measures for music. In: Proc. of DAFx 2003, pp. 7–12 (2003)
Peter, C., Ebert, E., Beikirch, H.: A wearable multi-sensor system for mobile acquisition of emotion-related physiological data. In: Tao, J., Tan, T., Picard, R.W. (eds.) ACII 2005. LNCS, vol. 3784, pp. 691–698. Springer, Heidelberg (2005)
Lichtenstein, A., Oehme, A., Kupschick, S., Jürgensohn, T.: Comparing Two Emotion Models for Deriving Affective States from Physiological Data. In: Peter, C., Beale, R. (eds.) Affect and Emotion in Human-Computer Interaction. LNCS, vol. 4868. Springer, Heidelberg (2008)
Money, A.G., Agius, H.: Automating the extraction of emotion-related multimedia semantics. In: Workshop on The Role of Emotion in Human-Computer Interaction (2005)
Nagel, F., Grewe, O., Kopiez, R., Altenmller, E.: The relationship of psycho-physiological responses and self-reported emotions while listening to music. In: Proc. of the 30th Göttingen Neurobiology Conference (2005)
Livingstone, S.R., Brown, A.R., Muhlberger, R.: Influencing the perceived emotions of music with intent. In: Proc. of the 3rd International Conference on Generative Systems (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Loviscach, J., Oswald, D. (2008). In the Mood: Tagging Music with Affects. In: Peter, C., Beale, R. (eds) Affect and Emotion in Human-Computer Interaction. Lecture Notes in Computer Science, vol 4868. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85099-1_19
Download citation
DOI: https://doi.org/10.1007/978-3-540-85099-1_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85098-4
Online ISBN: 978-3-540-85099-1
eBook Packages: Computer ScienceComputer Science (R0)