References
- Adorno, T. (1941). On popular music. Studies in Philosophy and Social Science, 9(1), 17–48.
- Agres, K., Abdallah, S., and Pearce, M. (2018). Information-theoretic properties of auditory sequences dynamically influence expectation and memory. Cognitive Science, 42(1), 43–76. 10.1111/cogs.12477
- Andrews, T. (2018, July 9). Billboard’s charts used to be our barometer for music success. Are they meaningless in the streaming age? The Washington Post.
https://www.washingtonpost.com/news/arts-and-entertainment/wp/2018/07/05/billboards-charts-used-to-be-our-barometer-for-music-success-are-they-meaningless-in-the-streaming-age/ [Accessed: 2023-02-18]. - Arthur, C., and Condit-Schultz, N. (2023). The coordinated corpus of popular musics (CoCoPops): A meta-corpus of melodic and harmonic transcriptions. In Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR), Milan, Italy.
- Barrett, F., Grimm, K., Robins, R., Wildschut, T., Sedikides, C., and Janata, P. (2010). Music-evoked nostalgia: Affect, memory, and personality. Emotion, 10(3), 390–403. 10.1037/a0019006
- Bartlett, J., and Dowling, W. (1980). Recognition of transposed melodies: A key-distance effect in developmental perspective. Journal of Experimental Psychology: Human Perception and Performance, 6(3), 501–515. 10.1037/0096-1523.6.3.501
- Benetos, E., Dixon, S., Duan, Z., and Ewert, S. (2019). Automatic music transcription: An overview. IEEE Signal Processing Magazine, 36(1), 20–30. 10.1109/MSP.2018.2869928
- Bigand, E., Poulin, B., Tillmann, B., Madurell, F., and D’Adamo, D. (2003). Sensory versus cognitive components in harmonic priming. Journal of Experimental Psychology: Human Perception and Performance, 29(1), 159–171. 10.1037/0096-1523.29.1.159
- Brand, C. O., Acerbi, A., and Mesoudi, A. (2019). Cultural evolution of emotional expression in 50 years of song lyrics. Evolutionary Human Sciences, 1, e11. 10.1017/ehs.2019.11
- Burgoyne, J., Wild, J., and Fujinaga, I. (2011). An expert ground truth set for audio chord recognition and music analysis. In Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR), Miami, Florida.
- Clemente, A., Vila-Vidal, M., Pearce, M., Aguiló, G., Corradi, G., and Nadal, M. (2020). A set of 200 musical stimuli varying in balance, contour, symmetry, and complexity: Behavioral and computational assessments. Behavior Research Methods, 52, 1491–1509. 10.3758/s13428-019-01329-8
- Cuddy, L., and Lyons, H. (1981). Musical pattern recognition: A comparison of listening to and studying tonal structures and tonal ambiguities. Psychomusicology, 1(2), 15–33. 10.1037/h0094283
- Davidson, M., and Heartwood, K. (1996). Songwriting for Beginners. Alfred Music Publishing.
- Deruty, E., and Pachet, F. (2015). The MIR perspective on the evolution of dynamics in mainstream music. In Proceedings of the 16th International Society for Music Information Retrieval Conference (ISMIR), Malaga, Spain.
- Dodds, P. S., and Danforth, C. M. (2010). Measuring the happiness of large-scale written expression: Songs, blogs, and presidents. Journal of Happiness Studies, 11(4), 441–456. 10.1007/s10902-009-9150-9
- Donahue, C., Mao, H., and McAuley, J. (2018). The NES music database: A multi-instrumental dataset with expressive performance attributes. In Proceedings of the 19th International Conference on Music Information Retrieval (ISMIR), Paris, France.
- Dorfer, M., Hajič, J., Arzt, A., Frostel, H., and Widmer, G. (2018). Learning audio-sheet music correspondences for cross-modal retrieval and piece identification. Transactions of the International Society for Music Information Retrieval, 1(1), 22–33. 10.5334/tismir.12
- Egermann, H., Pearce, M., Wiggins, G., and McAdams, S. (2013). Probabilistic models of expectation violation predict psychophysiological emotional responses to live concert music. Cognitive, Affective, and Behavioral Neuroscience, 13(3), 533–553. 10.3758/s13415-013-0161-y
- Everett, W. (1999). The Beatles as Musicians: Revolver through the Anthology. Oxford University Press.
- Everett, W. (2009). The Foundations of Rock: From ‘Blue Suede Shoes’ to ‘Suite: Judy Blue Eyes’. Oxford University Press.
- Gingras, B., Pearce, M., Goodchild, M., Dean, R., Wiggins, G., and McAdams, S. (2016). Linking melodic expectation to expressive performance timing and perceived musical tension. Journal of Experimental Psychology: Human Perception and Performance, 42(4), 594–609. 10.1037/xhp0000141
- Gold, B., Pearce, M., Mas-Herrero, E., Dagher, A., and Zatorre, R. (2019). Predictability and uncertainty in the pleasure of music: A reward for learning? The Journal of Neuroscience, 39(47), 9397–9409. 10.1523/JNEUROSCI.0428-19.2019
- Gotham, M., Gullings, K., Hamm, C., Hughes, B., Jarvis, B., Lavengood, M., and Peterson, J. (2021). Open Music Theory. Viva Open Publishing.
https://viva.pressbooks.pub/openmusictheory/ - Goto, M., Hashiguchi, H., Nishimura, T., and Oka, R. (2002). RWC music database: Popular, classical, and jazz music databases. In Proceedings of the 3rd International Conference on Music Information Retrieval (ISMIR), Paris, France.
- Hawthorne, C., Stasyuk, A., Roberts, A., Simon, I., Huang, C.-Z. A., Dieleman, S., Elsen, E., Engel, J., and Eck, D. (2019). Enabling factorized piano music modeling and generation with the MAESTRO dataset. In International Conference on Learning Representations, New Orleans, Louisiana.
- Interiano, M., Kazemi, K., Wang, L., Yang, J., Yu, Z., and Komarova, N. L. (2018). Musical trends and predictability of success in contemporary songs in and out of the top charts. Royal Society Open Science, 5(5), 171274. 10.1098/rsos.171274
- Janata, P., Tomic, S., and Rakowski, S. (2007). Characterisation of music-evoked autobiographical memories. Memory, 15(8), 845–860. 10.1080/09658210701734593
- Ji, S., Luo, J., and Yang, X. (2020). A comprehensive survey on deep music generation: Multi-level representations, algorithms, evaluations, and future directions. arXiv preprint arXiv:2011.06801.
- Kong, Q., Li, B., Chen, J., and Wang, Y. (2022). GiantMIDI-Piano: A large-scale MIDI dataset for classical piano music. Transactions of the International Society for Music Information Retrieval, 5(1), 87–98.
- Krumhansl, C. (1990). Cognitive Foundations of Musical Pitch. Oxford University Press.
- Lambert, B., Kontonatsios, G., Mauch, M., Kokkoris, T., Jockers, M., Ananiadou, S., and Leroi, A. (2020). The pace of modern culture. Natural Human Behavior, 4, 352–360. 10.1038/s41562-019-0802-4
- Li, M., Chen, X., Li, X., Ma, B., and Vitányi, P. (2004). The similarity metric. IEEE Transactions on Information Theory, 50, 3250–3264. 10.1109/TIT.2004.838101
- Mauch, M., MacCallum, R. M., Levy, M., and Leroi, A. M. (2015). The evolution of popular music: USA 1960–2010. Royal Society Open Science, 2(5), 150081. 10.1098/rsos.150081
- Middleton, R. (1990). Studying Popular Music. Open University Press.
- Molanphy, C. (2013, August 1). How the Hot 100 became America’s hit barometer. National Public Radio.
https://www.npr.org/sections/therecord/2013/08/16/207879695/how-the-hot-100-became-americas-hit-barometer#:~:text=After%20three%20years%20of%20tinkering,radio%20airplay%20and%20song%20sales [Accessed: 2023-02-18]. - Müller, M., Konz, V., Bogler, W., and Arifi-Müller, V. (2011). Saarland music data (SMD). In Late-Breaking and Demo Session of the 12th International Society for Music Information Retrieval Conference (ISMIR), Miami, Florida.
- Negus, K. (1997). Popular Music in Theory: An Introduction. Wesleyan University Press.
- Palmer, C., and Krumhansl, C. (1990). Mental representation for musical meter. Journal of Experimental Psychology: Human Perception and Performance, 16, 728–741. 10.1037/0096-1523.16.4.728
- Patel, A. D., and Daniele, J. R. (2003). An empirical comparison of rhythm in language and music. Cognition, 87(1), B35–B45. 10.1016/s0010-0277(02)00187-7
- Pearce, M. (2005). The construction and evaluation of statistical models of melodic structure in music perception and composition. PhD thesis, School of Informatics, City University, London, UK.
- Pearce, M. (2018). Statistical learning and probabilistic prediction in music cognition: Mechanisms of stylistic enculturation. Annals of the New York Academy of Sciences, 1423, 378–395. 10.1111/nyas.13654
- Pearce, M., and Müllensiefen, D. (2017). Compression-based modelling of musical similarity. Journal of New Music Research, 46(2), 135–155. 10.1080/09298215.2017.1305419
- Pearce, M., and Wiggins, G. (2006). Expectation in melody: The influence of context and learning. Music Perception, 23(5), 377–405. 10.1525/mp.2006.23.5.377
- Plantiga, J., and Trainor, L. (2005). Memory for melody: Infants use a relative pitch code. Cognition, 98, 1–11. 10.1016/j.cognition.2004.09.008
- Raffel, C. (2016). Learning-based methods for comparing sequences, with applications to Audio-to-MIDI alignment and matching. PhD thesis, Columbia University, New York.
- Saffran, J., Johnson, E., Aslin, R., and Newport, E. (1999). Statistical learning of tone sequences by human infants and adults. Cognition, 70(1), 27–52. 10.1016/S0010-0277(98)00075-4
- Salamon, J., and Urbano, J. (2012). Current challenges in the evaluation of predominant melody extraction algorithms. In Proceedings of the 13th International Society for Music Information Retrieval Conference (ISMIR), Porto, Portugal.
- Schmuckler, M. (1989). Expectation in music: Investigation of melodic and harmonic processes. Music Perception, 7(2), 109–149. 10.2307/40285454
- Sears, D., Pearce, M., Spitzer, J., Caplin, W., and McAdams, S. (2019). Expectations for tonal cadences: Sensory and cognitive priming effects. Quarterly Journal of Experimental Psychology, 72, 1422–1438. 10.1177/174702181881447
- Serrà, J., Corral, Á., Boguñá, M., Haro, M., and Arcos, J. L. (2012). Measuring the evolution of contemporary Western popular music. Scientific Reports, 2(1), 1–6. 10.1038/srep00521
- Shuker, R. (2013). Understanding Popular Music. Routledge.
- Stanley, B. (2013). Yeah Yeah Yeah: The Story of Modern Pop. Faber & Faber.
- Tillmann, B., and McAdams, S. (2004). Implicit learning of musical timbre sequences: Statistical regularities confronted with acoustic (dis)similarities. Journal of Experimental Psychology: Learning, Memory and Cognition, 30(5), 1131–1142. 10.1037/0278-7393.30.5.1131
- Trust, G. (2023, July 27). Happy birthday, Billboard charts! On July 27, 1940, the first song sales survey debuted. Billboard.
https://www.billboard.com/pro/happy-birthday-billboard-charts/ [Accessed: 2023-12-02]. - von Appen, R., and Frei-Hauenschild, M. (2015). AABA, refrain, chorus, bridge, prechorus: Song forms and their historical development. Samples: Online Publication of the German Society for Popular Music Studies, 13, 1–83. 10.22029/jlupub-850
- Wang, Z., Chen, K., Jiang, J., Zhang, Y., Xu, M., Dai, S., Bin, G., and Xia, G. (2020). Pop909: A pop-song dataset for music arrangement generation. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR), Montréal, Canada.
- Watson, C. J. (2003). The Everything Songwriting Book: All You Need to Create and Market Hit Songs. Simon and Schuster.