Computer-Based Music and Algorithmic Analysis: Historical Development

Computer-based music composition and analysis have roots stretching back to the mid-20th century. Calculation and formal algorithms have influenced composition for centuries, but the digital era brought new possibilities (Edwards, 2011). One early milestone was Lejaren Hiller and Leonard Isaacson’s Illiac Suite (1956) – the first known computer-assisted composition, generated on the ILLIAC I computer (Edwards, 2011). Around the same time, pioneers like Iannis Xenakis began applying mathematical models to music. Xenakis, an engineer-composer, used stochastic processes on early computers (e.g., IBM 7090) to compose pieces such as Pithoprakta (1956) (Edwards, 2011). His work exemplified algorithmic composition by treating the computer as a “pilot” to explore musical structures, generating material which the composer could then accept or modify (Edwards, 2011).

Another often-cited landmark is Alan Turing’s 1951 experiments, where a Ferranti Mark I computer was programmed to play tunes like “God Save the King.” This occurred years before the more famous 1957 Bell Labs synthesis experiments, establishing Turing’s overlooked but leading role in early computer music (Copeland, Long, & Carpenter, 2017). Throughout the 1960s and 1970s, algorithmic and computer music research expanded: composers and researchers developed formalism for composition (e.g., Hiller’s later works, Max Mathews at Bell Labs) and began using computers to analyze music as well, anticipating today’s data-driven musicology. By the late 20th century, the field had grown into what we now call computational musicology (Edwards, 2011).

Methodologically, early work relied on custom algorithms and nascent hardware. Hiller’s Illiac Suite, for example, used Markov chains and probability rules coded in FORTRAN (Edwards, 2011). Xenakis developed the Stochastic Music Program, translating statistical formulas into scores (Edwards, 2011). These efforts laid the groundwork for later statistical analysis of music. Over time, as computing power grew, focus expanded beyond composition to analysis – identifying patterns in melody, harmony, and rhythm across large corpora (Marion et al., 2025; Llorens et al., 2023).

J-Core (Japanese Hardcore): Evolution and Musical Characteristics

J-Core, short for Japanese hardcore, is a high-speed electronic music style that emerged in Japan in the late 1990s. It is essentially the Japanese take on hardcore techno, influenced by the European gabber/happy hardcore scene but blended with Japanese pop culture. J-Core is characterized by an extreme tempo, frenetic energy, and a distinct otaku aesthetic. It commonly features fast breakbeats (~180–200 BPM) and liberally samples media from video games and anime (Host, 2015; Jenkins, 2018). In fact, it is marked by the use of high-pitched “chipmunk” voices and bright chiptune-style melodies over aggressive beats (Host, 2015).

History and development: J-Core took shape in the late 1990s during Japan’s rave and game culture boom (Jenkins, 2018). Early innovators like DJ Sharpnel, DJ Technorch, m1dy, and REDALiCE began splicing UK/US rave and Rotterdam gabber sounds with J-pop, anime soundtrack snippets, and game effects (Host, 2015). For example, DJ Sharpnel’s 1998 track “Project Gabbangelion” set a stylistic template with its 200+ BPM kickdrums and eclectic sampling (Host, 2015). Unlike typical club-oriented hardcore, J-Core tracks often lack a predictable dancefloor structure, opting for constant variation and abrupt transitions (Host, 2015).

Throughout the 2000s, J-Core grew via underground communities and internet platforms. The term “J-Core” gained popularity around 2006, and netlabels like HARDCORE TANO*C helped solidify the genre’s identity (Jenkins, 2018). J-Core became associated with rhythm games (e.g., Beatmania IIDX), which increased average tempos and encouraged experimentation (Kishi, Shioya, & Nakabayashi, 2024). A recent analysis comparing Japanese and UK hardcore shows that J-Core tends to be faster (average 186–187 BPM) and more frequently in major keys, contributing to its “brighter” tonality (Kishi et al., 2024).

Reference list

Edwards, M. (2011). Algorithmic composition: Computational thinking in music. Communications of the ACM, 54(7), 58–65.

Copeland, J., Long, J., & Carpenter, B. (2017). Turing and the history of computer music. In J. Copeland et al. (Eds.), The Turing Guide (pp. 219–230). Oxford University Press.

Kishi, N., Shioya, R., & Nakabayashi, Y. (2024). Development of a system for classifying J-Core and UK Hardcore music genres using music2vec. Proceedings of the ICCM 2024 Conference.

Host, V. (2015, January 19). A kick in the kawaii: Inside the world of J-Core. Red Bull Music Academy Daily.

Jenkins, D. (2018, April 26). Beyond J-Core: An introduction to the real sound of Japanese hardcore. Bandcamp Daily.

Marion, G., Gao, F., Gold, B. P., Di Liberto, G. M., & Shamma, S. (2025). IDyOMpy: A new Python-based model for the statistical analysis of musical expectations. Journal of Neuroscience Methods, 415, 110347.

Llorens, A., Simonetta, F., Serrano, M., & Torrente, Á. (2023). musif: A Python package for symbolic music feature extraction. arXiv preprint arXiv:2307.01120.

Eck, S. O. (2023). Interactive Music Analysis Tool (I-MaT). In Proceedings of JADH 2023 – Digital Humanities Conference.

Defferrard, M., Benzi, K., Vandergheynst, P., & Bresson, X. (2017). FMA: A dataset for music analysis. In Proceedings of the 18th International Society for Music Information Retrieval Conference (ISMIR) (pp. 316–323).

Bogdanov, D., Won, M., Tovstogan, P., Porter, A., & Serra, X. (2019). The MTG-Jamendo dataset for automatic music tagging. In Proceedings of the Machine Learning for Music Discovery Workshop, ICML 2019.

Raffel, C., & Ellis, D. P. W. (2016). Large-scale analysis of MIDI data: The Lakh MIDI Dataset. In Proceedings of the 17th International Society for Music Information Retrieval Conference (ISMIR).