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].
  3. Taruskin, R. (2011). Catching Up with Rimsky-Korsakov. Music Theory Spectrum, 33(2), 169–185.
  4. Blau, A. (2011). Uncertainty and the History of Ideas. History and Theory, 50(3), 358–372.
  5. Yarkoni, T. (2019). The Generalizability Crisis. PsyArXiv.
  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.
  7. Harrison, P., & Pearce, M. (2020). Representing Harmony in Computational Music Cognition. PsyArXiv.
  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.
  9. Kania, D., Kania, P., & Łukaszewicz, T. (2021). Trajectory of Fifths in Music Data Mining. IEEE Access, 9, 8751–8761.
  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.
  11. Morgan, R. P. (2003). The Concept of Unity and Musical Analysis. Music Analysis, 22(12), 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.
  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.
  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.
  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.
  16. Nakamura, E., & Kaneko, K. (2019). Statistical Evolutionary Laws in Music Styles. Scientific Reports, 9(1), 15993.
  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.
  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.
  19. Lumaca, M., & Baggio, G. (2017). Cultural Transmission and Evolution of Melodic Structures in Multi-generational Signaling Games. Artificial Life, 23(3), 406–423.
  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.
  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.
  22. Evans, T. S., & Giometto, A. (2011). Turnover Rate of Popularity Charts in Neutral Models. ArXiv:1105.4044 [Physics].
  23. Kandler, A., & Powell, A. (2018). Generative inference for cultural evolution. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1743), 20170056.
  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.
  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.
  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.

Already read


  1. Harte, J. (2011). Maximum Entropy and Ecology: A Theory of Abundance, Distribution, and Energetics.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  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.
  12. Fafinski, M., & Piotrowski, M. (2021). Modelling Medieval Vagueness. In INFORMATIK 2020 (pp. 1317–1326). Gesellschaft für Informatik.
  13. Ewell, P. A. (2020). Music Theory and the White Racial Frame. Music Theory Online, 26(2).
  14. Horton, J. (2020). On the Musicological Necessity of Musical Analysis. The Musical Quarterly, 103(1–2), 62–104.
  15. Guest, O., & Martin, A. E. (2021). How Computational Modeling Can Force Theory Building in Psychological Science. Perspectives on Psychological Science, 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.
  17. Manovich, L. (2016). The Science of Culture? Social Computing, Digital Humanities and Cultural Analytics. Journal of Cultural Analytics, 11060.
  18. Kania, D., Kania, P., & Łukaszewicz, T. (2021). Trajectory of Fifths in Music Data Mining. IEEE Access, 9, 8751–8761.
  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.
  20. Youngblood, M. (2019a). Cultural transmission modes of music sampling traditions remain stable despite delocalization in the digital age. PLOS ONE, 14(2), e0211860.
  21. Youngblood, M. (2019b). Conformity bias in the cultural transmission of music sampling traditions. Royal Society Open Science, 6(9), 191149.
  22. Arten, S. (2018). The origin of fixed-scale solmization in The Whole Booke of Psalmes. Early Music, 46(1), 149–165.
  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.
  24. Ballance, J. (2020). Pitch-Class Distributions in the Music of Anton Webern. CHR 2020: Workshop on Computational Humanities Research, 214–224.