Innovative Musician Selected by AI: Rachmaninoff

Portrait of Ludwig van Beethoven (Source: Work by Joseph Karl Stieler)

[Asia Economy Reporter Junho Hwang] The most innovative classical musician selected by artificial intelligence was Sergey Rachmaninoff (Russia). The musician who had the greatest influence on the development of later music was Ludwig van Beethoven (Germany).

KAIST announced on the 4th that a research team led by Professor Juyong Park of the Graduate School of Culture Technology developed a theoretical physics algorithm that calculates the innovativeness and influence of human cultural and artistic creations based on network science and big data. This technology is significant in that it presents a way to scientifically evaluate the creativity of artworks through big data.

Influence belongs to Beethoven, Innovation to Rachmaninoff

The research team quantitatively demonstrated the creativity and innovativeness of classical musicians' works through this algorithm.

The team extracted 'codewords' made of simultaneously played pitches from Western piano scores composed between 1700 and 1900. Then, they measured the similarity between works to create a network showing how much the works influenced each other. Through this, they quantitatively calculated how innovative each work was and how much influence it had on later works.

Through this research, the representative composers of the Baroque and Classical periods (1710-1800) were identified as Handel, Haydn, and Mozart. After the Classical-Romantic transition period (1800-1820), Beethoven was recognized as the composer with the greatest influence. Celebrating his 250th birth anniversary this year, Beethoven maintained the highest influence for nearly 100 years after his death. The process by which Romantic era (1820-1910) masters such as Liszt and Chopin emerged under Beethoven's influence was also quantitatively clarified.

The research team also revealed that Rachmaninoff, a master of the late Romantic period, was the most innovative composer who constantly attempted to differentiate himself not only from past conventions but also from his own works.

Quantitative Measurement Possible in Other Art Fields

The research team expects that this method can also be applied to the study of creativity in literary works made of words, sentences, colors, and patterns, as well as visual arts such as painting, architecture, and design.

Professor Juyong Park said, "We have shown that the difficult problem of evaluating creativity, which has been a barrier to the scientific study of cultural and artistic creations, can be solved by utilizing network science and big data. Especially in a situation where the role of computers in cultural and artistic creation is increasing, this will greatly help the development of artificial creativity that maximizes human creativity and aesthetic sensibility by overcoming the limitations of artificial intelligence that merely imitates human calculation ability."

This content was produced with the assistance of AI translation services.