Anthony is a publishing academic in empirical musicology and music psychology. He completed his PhD at the UNSW Faculty of Arts and Social Sciences in 2019, and is working as a Casual Academic and Research Associate at the UNSW Empirical Musicology Laboratory. He also holds positions as a Research Fellow for the University of Padova, Italy (remote position in the Sound and Music Computing Group, Department of Information Engineering), and as a Research Assistant at the MARCS Institute for Brain, Behaviour & Development (Western Sydney University). Anthony's research is related to music aesthetics and perception, automated music recommendation systems, digitisation methods for the preservation of analogue audio recordings, performance practices, and voice acoustics.
Chmiel, A., & Schubert, E. (2020). Imaginative enrichment produces higher preference for unusual music than historical framing: A literature review and two empirical studies. Frontiers in Psychology. doi:10.3389/fpsyg.2020.01920
Murari, M., Chmiel, A., Tiepolo, E., Zhang, J. D., Canazza, S., Rodà, A., & Schubert, E. (2020). Key clarity is blue, relaxed, and maluma: Machine learning used to discover cross-modal connections between sensory items and the music they spontaneously evoke. 2020 International Conference on Kansei Engineering and Emotion Research (KEER 2020), Tokyo, Japan.
Canazza, S., Fantozzi, C., Pretto, N., Rodà, A., Chmiel, A., & Schubert, E. (2019). Quelle voci poco fa: l’intelligenza artificiale a contrastare l’eclisse delle memorie sonore [English translation: Those voices now gone - Artificial intelligence to counteract the eclipse of sound memories]. 1st Convegno Nazionale CINI sull'Intelligenza Artificiale (Ital-IA), March 19, Rome, Italy.
Chmiel, A., & Schubert, E. (2019). Psycho-historical contextualization for music and visual works: A literature review and comparison between artistic mediums. Frontiers in Psychology, 10(182). doi:10.3389/fpsyg.2019.00182
Chmiel, A., & Schubert, E. (2019). Unusualness as a predictor of music preference. Musicae Scientiae, 23(4) 426-441. doi:10.1177/1029864917752545
Chmiel, A., & Schubert, E. (2018). Emptying rooms: When the inverted-U model of preference fails—An investigation using music with collative extremes. Empirical Studies of the Arts, 36(2), 199-221.
Chmiel, A., & Schubert, E. (2018). Remembering the forgetting curve: A simulation and new explanation of the inverted-U preference trajectory for exposure to music. In E. Himonides, A. King, & F. Cuadrado (Eds.), Proceedings of the Sempre MET2018: Researching music, education, technology (pp. 135-139). London, United Kingdom: iMerc
Chmiel, A., & Schubert, E. (2018). A simple algorithm for music recommendation, built on established psychological principles. In M. D. Ventura (Ed.), New music concepts: 5th International conference, ICNMC 2018. Treviso, Italy: Abeditore.
Chmiel, A., & Schubert, E. (2018). Using psychological principles of memory storage and preference to improve music recommendation systems. Leonardo Music Journal, 28, 77-81.
Chmiel, A., & Schubert, E. (2017). Back to the inverted-U for music preference: A review of the literature. Psychology of Music, 45(6), 886-909.
Johnson-Read, L., Chmiel, A., Schubert, E., & Wolfe, J. (2015). Performing lieder: Expert perspectives and comparison of vibrato and singer's formant with opera singers. Journal of Voice, 29(5), 645. e615-645. e632.
Chmiel, A. (2020). Guide to creating a server for online R experiments using psychTestR. Technical report for the MARCS Online Platforms Committee, formed in response to Covid-19. http://dx.doi.org/10.13140/RG.2.2.18849.43360
Chmiel, A. (2019). Psycho-historical contextualization versus imaginative engagement for music preference. Graduate Keynote presented at the International Symposium on Performance Science (ISPS 2019), July 16-20, Melbourne, Australia.
Chmiel, A. (2019). Theory, data, and application of psychological principles for music preference. (Unpublished Doctoral Dissertation), University of New South Wales, Sydney, Australia.
Chmiel, A. (2019). Theory, data, and application of psychological principles for music preference. Lecture presented at the Department of Information Engineering. April 4, University of Padova, Italy.
Chmiel, A. (2018). A simple algorithm for music recommendation, built on established psychological principles. Lecture presented at the Department of Information Engineering. March 15, University of Padova, Italy.
Chmiel, A. (2018). The new U: Music preference is an inverted-U as a function of exposure by reinventing the Ebbinghaus memory retention curve. In Veloso, R., Chmiel, A., Zhang, J. D., Burwell, K., & Schubert, E. (Eds.), Proceedings of the 15th International Conference on Music Perception and Cognition (ICMPC15) combined with the 10th triennial conference of the European Society for the Cognitive Sciences of Music (ESCOM10): Sydney Hub. July 23-28, Sydney, Australia.
Chmiel, A. (2017). Beyond similarity: Applying collative variables and the inverted-U to music recommendation systems. In Veloso, R., Chmiel, A., Susino, M., Dickson, T., & Schubert, E. (Eds.), Proceedings of the inaugural Global Arts and Psychology Seminar (GAPS2017). April 28-29, Sydney, Australia.
Chmiel, A. (2017). Take it to the limit: Investigating preference for extreme music. Paper presented at the 3rd Conference of the Australian Music and Psychology Society (AMPS). December 7-9, Brisbane, Australia.