References
- Allan, D. (2006). Effects of popular music in advertising on attention and memory. Journal of Advertising Research, 46(4), 434–444. https://doi.org/10.2501/S0021849906060491
- Asparouhov, T., & Muthén, B. (2009). Exploratory structural equation modeling. Structural Equation Modeling: A Multidisciplinary Journal, 16(3), 397–438. https://doi.org/10.1080/10705510903008204
- Bonneville-Roussy, A., Rentfrow, P. J., Xu, M. K., & Potter, J. (2013). Music through the ages: Trends in musical engagement and preferences from adolescence through middle adulthood. Journal of Personality and Social Psychology, 105(4), 703–717. https://doi.org/10.1037/a0033770
- Bronner, K., & Hirt, R. (Eds.). (2009). Audio branding: Brands, sound and communication. Nomos.
- Browne, M. W. (2001). An overview of analytic rotation in exploratory factor analysis. Multivariate Behavioral Research, 36(1), 111–150. https://doi.org/10.1207/S15327906MBR3601_05
- Brunswik, E. (1952). The conceptual framework of psychology. University of Chicago Press.
- Burke, R. (2000). Knowledge-based recommender systems. In M. Dekker (Ed.), Encyclopedia of library and information science (Vol. 69, pp. 180).
- Burmann, C., Jost-Benz, M., & Riley, N. (2009). Towards an identity-based brand equity model. Journal of Business Research, 62(3), 390–397. https://doi.org/10.1016/j.jbusres.2008.06.009
- Burred, J. J., & Peeters, G. (2009). An adaptive system for music classification and tagging. Proceedings of the LAS – learning the semantics of audio signals, Graz, Austria, December 2, 2009.
- Chernatony, L. d. (1999). Brand management through narrowing the gap between brand identity and brand reputation. Journal of Marketing Management, 15(1–3), 157–179. https://doi.org/10.1362/026725799784870432
- Eerola, T., Friberg, A., & Bresin, R. (2013). Emotional expression in music: Contribution, linearity, and additivity of primary musical cues. Frontiers in Psychology, 4), https://doi.org/10.3389/fpsyg.2013.00487
- Eerola, T., Lartillot, O., & Toiviainen, P. (2009). Prediction of multidimensional emotion ratings in music from audio using multivariate regression models. Paper presented at the 10th international Society for music information retrieval Conference, Kobe, Japan, October 26–30, 2009.
- Egermann, H. (2019). Creating a brand image through music: Understanding the psychological mechanisms behind audio branding. In M. Grimshaw, M. Walther-Hansen, & M. Knakkergaard (Eds.), The Oxford Handbook of Sound and Imagination (Vol. 2). Oxford University Press. https://doi.org/10.1093/oxfordhb/9780190460242.013.29.
- Egermann, H., Fernando, N., Chuen, L., & McAdams, S. (2015). Music induces universal emotion-related psychophysiological responses: Comparing Canadian listeners to Congolese Pygmies. Frontiers in Psychology, 5, 1341. https://doi.org/10.3389/fpsyg.2014.01341
- Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037/1082-989X.4.3.272
- Gaus, H., Jahn, S., Kiessling, T., & Drengner, J. (2010). How to measure brand values? In M. C. Campbell, J. Inman, & R. Pieters (Eds.), NA - Advances in consumer research volume 37 (pp. 697–698). Association for Consumer Research.
- Geuens, M., Weijters, B., & De Wulf, K. (2009). A new measure of brand personality. International Journal of Research in Marketing, 26(2), 97–107. https://doi.org/10.1016/j.ijresmar.2008.12.002
- Gingras, B., Marin, M. M., & Fitch, W. T. (2014). Beyond intensity: Spectral features effectively predict music-induced subjective arousal. Quarterly Journal of Experimental Psychology, 67(7), 1428–1446. https://doi.org/10.1080/17470218.2013.863954
- Gorn, G. J. (1982). The effects of music in advertising on choice behavior: A classical conditioning approach. Journal of Marketing, 46(1), 94–101. https://doi.org/10.1177/002224298204600109
- Gustafsson, C. (2015). Sonic branding: A consumer-oriented literature review. Journal of Brand Management, 22(1), 20–37. https://doi.org/10.1057/bm.2015.5
- Han, B., Ho, S., Dannenberg, R., & Hwang, E. (2009). SMERS: Music emotion recognition using support vector regression. Computer Science Department. http://repository.cmu.edu/compsci/514.
- Herzog, M., Lepa, S., & Egermann, H. (2016). Towards automatic music recommendation for audio branding scenarios. Proceedings of the 17th international Society for music information retrieval Conference (ISMIR), New York City, USA, August 7–11, 2016.
- Herzog, M., Lepa, S., Egermann, H., Schoenrock, A., & Steffens, J. (2020). Towards a common terminology for music branding campaigns. Journal of Marketing Management, 36(1–2), 176–209. https://doi.org/10.1080/0267257X.2020.1713856
- Herzog, M., Lepa, S., Steffens, J., Egermann, H., & Schönrock, A. (2018). How do musical means of expression affect the perception of musical meaning? ICMPC/ESCOM. Paper presented at the ICMPC15/ESCOM10, Graz, Austria, 23–28 July, 2018.
- Herzog, M., Lepa, S., Steffens, J., Schönrock, A., & Egermann, H. (2017). Predicting musical meaning in audio branding scenarios. Proceedings of the 25th anniversary conference of the European society for the cognitive sciences of music (ESCOM), Ghent, Belgium, July 31–August 4, 2017.
- Homma, N., & Ulktzhöffer, J. (1990). The internationalisation of everyday-life-research: Markets and milieus. Marketing and Research Today, 18, 197–207.
- Hothorn, T., Hornik, K., Strobl, C., & Zeileis, A. (2019). Party: A laboratory for recursive partytioning (Version 1.3-3). https://CRAN.R-project.org/package=party.
- Hung, K. (2000). Narrative music in congruent and incongruent TV advertising. Journal of Advertising, 29(1), 25–34. https://doi.org/10.1080/00913367.2000.10673601
- Jackson, D. M. (2003). Sonic branding: An introduction ( P. Fulberg, Hrsg.). Palgrave Macmillan.
- Juslin, P. N. (2000). Cue utilization in communication of emotion in music performance: Relating performance to perception. Journal of Experimental Psychology: Human Perception and Performance, 26(6), 1797–1812. https://doi.org/10.1037/0096-1523.26.6.1797
- Kilian, K. (2009). From brand identity to audio branding. In K. Bronner & R. Hirt (Hrsg.), Audio branding: Brands, sound and communication (pp. 35–48). Nomos.
- Larsen, G., Lawson, R., & Todd, S. (2010). The symbolic consumption of music. Journal of Marketing Management, 26(7/8), 671–685. https://doi.org/10.1080/0267257X.2010.481865
- Leman, M., Vermeulen, V., De Voogdt, L., Moelants, D., & Lesaffre, M. (2005). Prediction of musical affect using a combination of acoustic Structural cues. Journal of New Music Research, 34(1), 39–67. https://doi.org/10.1080/09298021050123978
- MacInnis, D. J., & Park, C. W. (1991). The differential role of characteristics of music on high- and Low-Involvement consumers’ Processing of ads. Journal of Consumer Research, 18(2), 161–173. https://doi.org/10.1086/209249
- Müllensiefen, D., & Baker, D. J. (2015). Music, brands, & advertising: Testing what works. In K. Bronner, C. Ringe, & R. Hirt (Hrsg.), Audio branding academy yearbook 2014/2015 (pp. 31–51). Nomos.
- Muthén, L. K., & Muthén, B. O. (2010). Mplus user’s guide. Statistical analysis with latent variables. (6th ed.). Muthén & Muthén.
- Nandan, S. (2005). An exploration of the brand identity–brand image linkage: A communications perspective. Journal of Brand Management, 12(4), 264–278. https://doi.org/10.1057/palgrave.bm.2540222
- North, A. C., Mackenzie, L. C., Law, R. M., & Hargreaves, D. J. (2004). The effects of musical and voice “fit” on responses to advertisements. Journal of Applied Social Psychology, 34(8), 1675–1708. https://doi.org/10.1111/j.1559-1816.2004.tb02793.x
- North, A. C., Sheridan, L. P., & Areni, C. S. (2016). Music congruity effects on product memory, perception, and choice. Journal of Retailing, 92(1), 83–95. https://doi.org/10.1016/j.jretai.2015.06.001
- Oakes, S. (2007). Evaluating empirical research into music in advertising: A congruity perspective. Journal of Advertising Research, 47(1), 38–50. https://doi.org/10.2501/S0021849907070055
- Oakes, S., & North, A. (2006). The impact of background musical tempo and timbre congruity upon ad content recall and affective response. Applied Cognitive Psychology, 20(4), 505–520. https://doi.org/10.1002/acp.1199
- Papadopoulos, H., & Peeters, G. (2011). Joint estimation of chords and downbeats from an audio signal. IEEE Transactions on Audio, Speech, and Language Processing, 19(1), 138–152. https://doi.org/10.1109/TASL.2010.2045236
- Peeters, G. (2004). A large set of audio features for sound description (similarity and classification) in the CUIDADO project [online]. IRCAM website: http://recherche.ircam.fr/anasyn/peeters/ARTICLES/Peeters_2003_cuidadoaudiofeatures.pdf.
- Peeters, G. (2006a). Chroma-based estimation of musical key from audio-signal analysis. Proceedings of the 7th international conference on music information retrieval (ISMIR 2006), Victoria, Canada, 8–12 October, 2006.
- Peeters, G. (2006b). Template-based estimation of time-varying tempo. EURASIP Journal on Advances in Signal Processing, 2007, article ID 67215. https://doi.org/10.1155/2007/67215
- Peeters, G. (2011). Spectral and temporal periodicity representations of rhythm for the automatic classification of music audio signal. IEEE Transactions on Audio, Speech, and Language Processing, 19(5), 1242–1252. https://doi.org/10.1109/TASL.2010.2089452
- Peeters, G., Cornu, F., Doukhan, D., Marchetto, E., Mignot, R., Perros, K., & Regnier, L. (2015). When audio features reach machine learning. International Conference on machine learning - Workshop on “machine learning for music discovery”. Presented at Lille, France, 5 July, 2015.
- Peeters, G., Giordano, B. L., Susini, P., Misdariis, N., & McAdams, S. (2011). The timbre toolbox: Extracting audio descriptors from musical signals. The Journal of the Acoustical Society of America, 130(5), 2902–2916. https://doi.org/10.1121/1.3642604
- Peeters, G., & Papadopoulos, H. (2011). Simultaneous beat and downbeat-tracking using a probabilistic framework: Theory and large-scale evaluation. IEEE Transactions on Audio, Speech, and Language Processing, 19(6), 1754–1769. https://doi.org/10.1109/TASL.2010.2098869
- Pichl, M., Zangerle, E., Specht, G., & Schedl, M. (2017). Mining culture-specific music listening Behavior from social Media data. 2017 IEEE international Symposium on Multimedia (ISM), Taichung, Taiwan, December 11–13, 2017.
- Saari, P., Fazekas, G., Eerola, T., Barthet, M., Lartillot, O., & Sandler, M. (2016). Genre-adaptive semantic computing and audio-based modelling for music mood Annotation. IEEE Transactions on Affective Computing, 7(2), 122–135. https://doi.org/10.1109/TAFFC.2015.2462841
- Schmidt, E. M., Turnbull, D., & Kim, Y. E. (2010). Feature selection for content-based, time-Varying musical emotion regression. Proceedings of the MIR’10, Philadelphia, PA, USA, March 29–31, 2010.
- Shevy, M. (2008). Music genre as cognitive schema: Extramusical associations with country and hip-hop music. Psychology of Music, 36(4), 477–498. https://doi.org/10.1177/0305735608089384
- SINUS. (2017). SINUS meta-milieus. Customization all over the world. Retrieved November 25, 2019, from https://www.sinus-institut.de/fileadmin/user_data/sinus-institut/Dokumente/downloadcenter/Sinus_Meta_Milieus/Working_with_Sinus-Meta-Milieus.pdf.
- Steenkamp, J.-B. E. M., & Baumgartner, H. (1998). Assessing measurement invariance in cross-national consumer research. Journal of Consumer Research, 25(1), 78–107. https://doi.org/10.1086/209528
- Steffens, J., Lepa, S., Herzog, M., Schönrock, A., & Egermann, H. (2018). “Bridging the semantic Gap” – Kann der semantische Ausdruck von Musik mithilfe von akustischen Signaleigenschaften vorhersagt werden? Fortschritte der Akustik. Proceedings of the DAGA-Meeting, München, March 19–22, 2018.
- Steffens, J., Lepa, S., Herzog, M., Schönrock, A., Peeters, G., & Egermann, H. (2017). High-level chord features extracted from audio can predict perceived musical expression. Proceedings of the 18th international Society for music information retrieval Conference (ISMIR), Suzhou, China, October 23–27, 2017.
- Strobl, C., Malley, J., & Tutz, G. (2009). An introduction to recursive partitioning: Rationale, application and characteristics of classification and regression trees, bagging and random forests. Psychological Methods, 14(4), 323–348. https://doi.org/10.1037/a0016973
- Tagg, P. (2013). Music’s meanings: A modern musicology for non-musos. Mass Media Music Scholar’s Press.
- Yang, Y.-H., Lin, Y.-C., Cheng, H.-T., Liao, I.-B., Ho, Y.-C., & Chen, H. H. (2008). Toward multi-modal music emotion classification. In Y.-M. R. Huang, C. Xu, K.-S. Cheng, J.-F. K. Yang, M. N. S. Swamy, S. Li, & J.-W. Ding (Hrsg.), Advances in multimedia information processing - PCM 2008 ( Bd. 5353, pp. 70–79). https://doi.org/10.1007/978-3-540-89796-5_8
- Zander, F. (2006). Musical influences in advertising: How music modifies first impressions of product endorsers and brands. Psychology of Music, 34(4), 465–480. https://doi.org/10.1177/0305735606067158