Reading Journal

On this page, you’ll find lists of things I have read recently that might give you an idea of my academic interests. My personal readings (books) can be found on Goodreads.

To read

  1. Ewell, P. (2020). Harmonic Functionalism in Russian Music Theory: A Primer. Theoria - Historical Aspects of Music Theory, 26, 61–84.
  2. Dai, A. M., Olah, C., & Le, Q. V. (2015). Document Embedding with Paragraph Vectors. ArXiv:1507.07998 [Cs]. http://arxiv.org/abs/1507.07998
  3. Taruskin, R. (2011). Catching Up with Rimsky-Korsakov. Music Theory Spectrum, 33(2), 169–185. https://doi.org/10.1525/mts.2011.33.2.169
  4. Blau, A. (2011). Uncertainty and the History of Ideas. History and Theory, 50(3), 358–372. https://doi.org/10.1111/j.1468-2303.2011.00590.x
  5. Yarkoni, T. (2019). The Generalizability Crisis. PsyArXiv. https://doi.org/10.31234/osf.io/jqw35
  6. Blei, D. M., Kucukelbir, A., & McAuliffe, J. D. (2017). Variational Inference: A Review for Statisticians. Journal of the American Statistical Association, 112(518), 859–877. https://doi.org/10.1080⁄01621459.2017.1285773
  7. Harrison, P., & Pearce, M. (2020). Representing Harmony in Computational Music Cognition. PsyArXiv. https://doi.org/10.31234/osf.io/xswp4
  8. Bikakis, A., Hyvönen, E., Jean, S., Markhoff, B., & Mosca, A. (2021). Editorial: Special issue on Semantic Web for Cultural Heritage. Semantic Web, Preprint(Preprint), 1–5. https://doi.org/10.3233/SW-210425
  9. Kania, D., Kania, P., & Łukaszewicz, T. (2021). Trajectory of Fifths in Music Data Mining. IEEE Access, 9, 8751–8761. https://doi.org/10.1109/ACCESS.2021.3049266
  10. Benjamin, W. E. (1982). Models of Underlying Tonal Structure: How Can They Be Abstract, and How Should They Be Abstract? Music Theory Spectrum, 4, 28–50. https://doi.org/10.2307⁄746008
  11. Morgan, R. P. (2003). The Concept of Unity and Musical Analysis. Music Analysis, 22(1⁄2), 7–50.
  12. Arthur, C. (2021). Vicentino versus Palestrina: A computational investigation of voice leading across changing vocal densities. Journal of New Music Research, 50(1), 74–101. https://doi.org/10.1080⁄09298215.2021.1877729
  13. Demany, L., Monteiro, G., Semal, C., Shamma, S., & Carlyon, R. P. (2021). The perception of octave pitch affinity and harmonic fusion have a common origin. Hearing Research, 108213. https://doi.org/10.1016/j.heares.2021.108213
  14. Micchi, G., Gotham, M., & Giraud, M. (2020). Not All Roads Lead to Rome: Pitch Representation and Model Architecture for Automatic Harmonic Analysis. Transactions of the International Society for Music Information Retrieval, 3(1), 42–54. https://doi.org/10.5334/tismir.45
  15. Turchin, P., Currie, T. E., Turner, E. A. L., & Gavrilets, S. (2013). War, space, and the evolution of Old World complex societies. Proceedings of the National Academy of Sciences, 110(41), 16384–16389. https://doi.org/10.1073/pnas.1308825110
  16. Nakamura, E., & Kaneko, K. (2019). Statistical Evolutionary Laws in Music Styles. Scientific Reports, 9(1), 15993. https://doi.org/10.1038/s41598-019-52380-6
  17. Warrell, J., Salichos, L., & Gerstein, M. (2020). Latent Evolutionary Signatures: A General Framework for Analyzing Music and Cultural Evolution. BioRxiv, 2020.10.23.352930. https://doi.org/10.1101⁄2020.10.23.352930
  18. Ellis, B. K., Hwang, H., Savage, P. E., Pan, B.-Y., Cohen, A. J., & Brown, S. (2018). Identifying style-types in a sample of musical improvisations using dimensional reduction and cluster analysis. Psychology of Aesthetics, Creativity, and the Arts, 12(1), 110–122. https://doi.org/10.1037/aca0000072
  19. Lumaca, M., & Baggio, G. (2017). Cultural Transmission and Evolution of Melodic Structures in Multi-generational Signaling Games. Artificial Life, 23(3), 406–423. https://doi.org/10.1162/ARTL_a_00238
  20. Acerbi, A., & Alexander Bentley, R. (2014). Biases in cultural transmission shape the turnover of popular traits. Evolution and Human Behavior, 35(3), 228–236. https://doi.org/10.1016/j.evolhumbehav.2014.02.003
  21. Crema, E. R., Edinborough, K., Kerig, T., & Shennan, S. J. (2014). An Approximate Bayesian Computation approach for inferring patterns of cultural evolutionary change. Journal of Archaeological Science, 50, 160–170. https://doi.org/10.1016/j.jas.2014.07.014
  22. Evans, T. S., & Giometto, A. (2011). Turnover Rate of Popularity Charts in Neutral Models. ArXiv:1105.4044 [Physics]. http://arxiv.org/abs/1105.4044
  23. Kandler, A., & Powell, A. (2018). Generative inference for cultural evolution. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1743), 20170056. https://doi.org/10.1098/rstb.2017.0056
  24. O’Dwyer, J. P., & Kandler, A. (2017). Inferring processes of cultural transmission: The critical role of rare variants in distinguishing neutrality from novelty biases. Philosophical Transactions of the Royal Society B: Biological Sciences, 372(1735), 20160426. https://doi.org/10.1098/rstb.2016.0426
  25. Wodak, R. (2015). Critical Discourse Analysis, Discourse-Historical Approach. In K. Tracy, C. Ilie, & T. Sandel (Eds.), The International Encyclopedia of Language and Social Interaction (p. 14pp). John Wiley & Sons. https://doi.org/10.4135⁄9780857028020.d6
  26. Reisigl, M. (2017). The Discourse-Historical Approach. In J. Flowerdew & J. E. Richardson (Eds.), The Routledge Handbook of Critical Discourse Studies. Routledge Handbooks Online. https://doi.org/10.4324⁄9781315739342.ch3

Already read

2021

  1. Harte, J. (2011). Maximum Entropy and Ecology: A Theory of Abundance, Distribution, and Energetics. https://doi.org/10.1093/acprof:oso/9780199593415.001.0001
  2. Nguyen, D., Liakata, M., DeDeo, S., Eisenstein, J., Mimno, D., Tromble, R., & Winters, J. (2020). How We Do Things With Words: Analyzing Text as Social and Cultural Data. Frontiers in Artificial Intelligence, 3. https://doi.org/10.3389/frai.2020.00062
  3. Hernando, A., Hernando, R., Plastino, A., & Plastino, A. R. (2013). The workings of the maximum entropy principle in collective human behaviour. Journal of The Royal Society Interface, 10(78), 20120758. https://doi.org/10.1098/rsif.2012.0758
  4. Harte, C. A., Sandler, M., Abdallah, S., & Gómez, E. (2005). Symbolic representation of musical chords: A proposed syntax for text annotations. Proceedings of the 4th International Conference on Music Information Retrieval (ISMIR), 56, 66–71.
  5. Raimond, Y., Abdallah, S., Sandler, M., Mary, Q., & Giasson, F. (2007). The Music Ontology. ISMIR 2007, 417–422.
  6. Fazekas, G., Raimond, Y., Jacobson, K., & Sandler, M. (2010). An Overview of Semantic Web Activities in the OMRAS2 Project. Journal of New Music Research, 39(4), 295–311. https://doi.org/10.1080⁄09298215.2010.536555
  7. Jones, J., de Siqueira Braga, D., Tertuliano, K., & Kauppinen, T. (2017). MusicOWL: The music score ontology. Proceedings of the International Conference on Web Intelligence, 1222–1229. https://doi.org/10.1145⁄3106426.3110325
  8. Rashid, S. M., De Roure, D., & McGuinness, D. L. (2018). A Music Theory Ontology. Proceedings of the 1st International Workshop on Semantic Applications for Audio and Music, 6–14. https://doi.org/10.1145⁄3243907.3243913
  9. Benestad, R. E., Nuccitelli, D., Lewandowsky, S., Hayhoe, K., Hygen, H. O., van Dorland, R., & Cook, J. (2016). Learning from mistakes in climate research. Theoretical and Applied Climatology, 126(3), 699–703. https://doi.org/10.1007/s00704-015-1597-5
  10. Błoch, A., Vasques Filho, D., & Bojanowski, M. (2020). Networks from archives: Reconstructing networks of official correspondence in the early modern Portuguese empire. Social Networks. https://doi.org/10.1016/j.socnet.2020.08.008
  11. Navarro-Cáceres, M., Caetano, M., Bernardes, G., Sánchez-Barba, M., & Merchán Sánchez-Jara, J. (2020). A Computational Model of Tonal Tension Profile of Chord Progressions in the Tonal Interval Space. Entropy, 22(11), 1291. https://doi.org/10.3390/e22111291
  12. Fafinski, M., & Piotrowski, M. (2021). Modelling Medieval Vagueness. In INFORMATIK 2020 (pp. 1317–1326). Gesellschaft für Informatik. https://doi.org/10.18420/inf2020_123
  13. Ewell, P. A. (2020). Music Theory and the White Racial Frame. Music Theory Online, 26(2). https://mtosmt.org/issues/mto.20.26.2/mto.20.26.2.ewell.html
  14. Horton, J. (2020). On the Musicological Necessity of Musical Analysis. The Musical Quarterly, 103(1–2), 62–104. https://doi.org/10.1093/musqtl/gdaa005
  15. Guest, O., & Martin, A. E. (2021). How Computational Modeling Can Force Theory Building in Psychological Science. Perspectives on Psychological Science, 1745691620970585. https://doi.org/10.1177⁄1745691620970585
  16. Nieuwkerk, M. van, Nijboer, H., & Kisjes, I. (2020). The Felix Meritis Concert Programs Database, 1832–1888: From Archival Ephemera to Searchable Performance Data: Arts and Media. Research Data Journal for the Humanities and Social Sciences, 5(2), 62–78. https://doi.org/10.1163⁄24523666-00502006
  17. Manovich, L. (2016). The Science of Culture? Social Computing, Digital Humanities and Cultural Analytics. Journal of Cultural Analytics, 11060. https://doi.org/10.22148⁄16.004
  18. Kania, D., Kania, P., & Łukaszewicz, T. (2021). Trajectory of Fifths in Music Data Mining. IEEE Access, 9, 8751–8761. https://doi.org/10.1109/ACCESS.2021.3049266
  19. Harte, C., Sandler, M., & Gasser, M. (2006). Detecting harmonic change in musical audio. Proceedings of the 1st ACM Workshop on Audio and Music Computing Multimedia - AMCMM ‘06, 21. https://doi.org/10.1145⁄1178723.1178727
  20. Youngblood, M. (2019a). Cultural transmission modes of music sampling traditions remain stable despite delocalization in the digital age. PLOS ONE, 14(2), e0211860. https://doi.org/10.1371/journal.pone.0211860
  21. Youngblood, M. (2019b). Conformity bias in the cultural transmission of music sampling traditions. Royal Society Open Science, 6(9), 191149. https://doi.org/10.1098/rsos.191149
  22. Arten, S. (2018). The origin of fixed-scale solmization in The Whole Booke of Psalmes. Early Music, 46(1), 149–165. https://doi.org/10.1093/em/cay003
  23. Bentley, R. A., Lipo, C. P., Herzog, H. A., & Hahn, M. W. (2007). Regular rates of popular culture change reflect random copying. Evolution and Human Behavior, 28(3), 151–158. https://doi.org/10.1016/j.evolhumbehav.2006.10.002
  24. Ballance, J. (2020). Pitch-Class Distributions in the Music of Anton Webern. CHR 2020: Workshop on Computational Humanities Research, 214–224. http://ceur-ws.org/Vol-2723/short5.pdf
  25. Mesoudi, A., & Thornton, A. (2018). What is cumulative cultural evolution? Proceedings of the Royal Society B: Biological Sciences, 285(1880), 20180712. https://doi.org/10.1098/rspb.2018.0712