Repurposing Music According to Individual Preferences for Personalized Soundtracks

Chapter
Part of the International Series on Computer Entertainment and Media Technology book series (ISCEMT)

Abstract

This chapter briefly explores another futuristic vision for videogame soundtracking: the automated selection and manipulation of existing soundtrack material. An early feature in the Grand Theft Auto series allowed players to use their own libraries of music in the car radio. Naturally this might be problematic if the musical selections of the players become incongruous with the gameplay narrative at a given point, but the feature was nonetheless well regarded and successful due to the fact that individual musical preferences are so powerful (hence, why different radio stations with genre specific preferences can even exist in the first place in the ‘real’ world). Of course, the Grand Theft Auto example benefitted slightly in terms of immersion as, just like in the real-world, the player might have less control over the exact selection of music which appeared on their radio regardless of the necessary soundtracking.

References

  1. ASA: American Standard Acoustical Terminology, Definition 12.9, Timbre. American Standards Association, New York (1960)Google Scholar
  2. Aures, W.: A procedure for calculating auditory roughness. Acust. 58, 268–281 (1985)Google Scholar
  3. Barrett, N.: Spatio-musical composition strategies. Organ. Sound. 7, 313–323 (2002)CrossRefGoogle Scholar
  4. Berger, K.W.: Some factors in the recognition of timbre. J. Acoust. Soc. Am. 36, 1888 (1964).  https://doi.org/10.1121/1.1919287 CrossRefGoogle Scholar
  5. Bresin, R., Friberg, A.: Emotional coloring of computer-controlled music performances. Comput. Music. J. 24, 44–63 (2000)CrossRefGoogle Scholar
  6. Chanel, G., Kronegg, J., Grandjean, D., Pun, T.: Emotion assessment: arousal evaluation using eeg’s and peripheral physiological signals. In: Gunsel, B., Jain, A.K., Tekalp, A.M., Sankur, B. (eds.) Multimedia Content Representation, Classification and Security, pp. 530–537. Springer LNCS (2006)Google Scholar
  7. Chanel, G., Ansari-Asl, K., Pun, T.: Valence-arousal evaluation using physiological signals in an emotion recall paradigm. In: Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on, pp. 2662–2667 (2007).  https://doi.org/10.1109/ICSMC.2007.4413638 Google Scholar
  8. Cook, P.R. (ed.): Experimental design in psychoacoustic research. In: Music, Cognition, and Computerized Sound: An Introduction to Psychoacoustics, pp. 299–320. MIT Press, Cambridge, MA (1999)Google Scholar
  9. Daly, I., Malik, A., Weaver, J., Hwang, F., Nasuto, S.J., Williams, D., Kirke, A., Miranda, E.: Identifying music-induced emotions from EEG for use in brain-computer music interfacing. In: IEEE, pp. 923–929 (2015),  https://doi.org/10.1109/ACII.2015.7344685
  10. Daly, I., Malik, A., Hwang, F., Roesch, E., Weaver, J., Kirke, A., Williams, D., Miranda, E., Nasuto, S.J.: Neural correlates of emotional responses to music: an EEG study. Neurosci. Lett. 573, 52–57 (2014)CrossRefGoogle Scholar
  11. Davies, S., Allen, P., Mann, M., Cox, T.J.: Musical moods: a mass participation experiment for affective classification of music. In: ISMIR, pp. 741–746 (2011)Google Scholar
  12. Disley, A.C., Howard, D.M., Hunt, A.D.: Timbral description of musical instruments. In: Proceedings of the 9th International Conference on Music Perception and Cognition, Bologna (2006)Google Scholar
  13. Eaton, J., Williams, D., Miranda, E.: The space between us: evaluating a multi-user affective brain-computer music interface. Brain Comput. Interf. 2, 103–116 (2015)CrossRefGoogle Scholar
  14. Fletcher, H.: Loudness, pitch and the timbre of musical tones and their relation to the intensity, the frequency and the overtone structure. J. Acoust. Soc. Am. 6, 59–69 (1934).  https://doi.org/10.1121/1.1915704 CrossRefGoogle Scholar
  15. Gabrielsson, A.: Acoustic correlates of emotionally expressive music. J. Acoust. Soc. Am. 100, 2778 (1996).  https://doi.org/10.1121/1.416426 CrossRefGoogle Scholar
  16. Hillenbrand, J.M., Clark, M.J., Houde, R.A.: Some effects of duration on vowel recognition. J. Acoust. Soc. Am. 108, 3013 (2000).  https://doi.org/10.1121/1.1323463 CrossRefGoogle Scholar
  17. Hillenbrand, J., Cleveland, R.A., Erickson, R.L.: Acoustic correlates of breathy vocal quality. J. Speech Hear. Res. 37, 769–778 (1994)CrossRefGoogle Scholar
  18. Iverson, P., Krumhansl, C.L.: Isolating the dynamic attributes of musical timbrea. J. Acoust. Soc. Am. 94, 2595–2603 (1993).  https://doi.org/10.1121/1.407371 CrossRefGoogle Scholar
  19. Johnson, C.G., Gounaropoulos, A.: Timbre interfaces using adjectives and adverbs. In: New Interfaces for Musical Expression, pp. 101–102. IRCAM, Paris (2006)Google Scholar
  20. Krumhansl, C.L.: Why is musical timbre so hard to understand? In: Structure and Perception of Electroacoustic Sound and Music, pp. 43–53. Elsevier, Amsterdam (1989)Google Scholar
  21. Lutfi, R.A.: Auditory detection of hollowness. J. Acoust. Soc. Am. 110, 1010 (2001).  https://doi.org/10.1121/1.1385903 CrossRefGoogle Scholar
  22. Malham, D.G.: Tutorial article: approaches to spatialisation. Organ. Sound. 3, 167–177 (1998)CrossRefGoogle Scholar
  23. Mcadams, S.: Perspectives on the contribution of timbre to musical structure. Comput. Music. J. 23, 85–102 (1999)CrossRefGoogle Scholar
  24. Nykanen, A., Johansson, O., Lundberg, J., Berg, J.: Modelling perceptual dimensions of saxophone sounds. Acust. United Acta Acust. 95, 539–549 (2009)CrossRefGoogle Scholar
  25. Pratt, R., Doak, P.: A subjective rating scale for timbre. J. Sound Vib. 45, 317–328 (1976).  https://doi.org/10.1016/0022-460X(76)90391-6 CrossRefGoogle Scholar
  26. Schouten, J.F.: The perception of timbre. In: Reports of the 6th International Congress on Acoustics, GP-6-2, pp.35–44, Tokyo (1968)Google Scholar
  27. Schubert, E., Wolfe, J.: Does timbral brightness scale with frequency and spectral centroid? Acta Acust. United Acust. 92, 820–825 (2006)Google Scholar
  28. Seashore, C.E.: The timbre of orchestral instruments. J. Acoust. Soc. Am. 6, 55 (1934).  https://doi.org/10.1121/1.1915695 CrossRefGoogle Scholar
  29. Stevens, S.S.: A scale for the measurement of the psychological magnitude pitch. J. Acoust. Soc. Am. 8, 185 (1937).  https://doi.org/10.1121/1.1915893 CrossRefGoogle Scholar
  30. Stevens, S.S., Harris, J.R.: The scaling of subjective roughness and smoothness. J. Exp. Psychol. 64, 489–494 (1962).  https://doi.org/10.1037/h0042621 CrossRefGoogle Scholar
  31. Terhardt, E.: On the perceptions of periodic sound fluctuation (roughness). Acust. 30, 201 (1974)Google Scholar
  32. Von Bismarck, G.: Sharpness as an attribute of the timbre of steady sounds. Acustica. 30, 172 (1974)Google Scholar
  33. Wedin, L., Goude, G.: Dimension analysis of the perception of instrumental timbre. Scand. J. Psychol. 13, 228–240 (1972).  https://doi.org/10.1111/j.1467-9450.1972.tb00071.x CrossRefGoogle Scholar
  34. Zwicker, E.: Subdivision of the audible frequency range into critical bands (Frequenzgruppen). J. Acoust. Soc. Am. 33, 248 (1961)CrossRefGoogle Scholar
  35. Zwicker, E., Fastl, H.: Psychoacoustics: Facts and Models, 2nd updated edn. Springer, New York (1999)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Digital Creativity LabsUniversity of YorkYorkUK

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