Bach, Beethoven and Brahms again? A computational view on the de facto canon of classical orchestral music in Germany and the USA at the beginning of the 21st century

• We collected classical music orchestra programs of two seasons in Germany and USA. • From this data we describe the current orchestral performance canon in both countries. • Machine Learning was used to infer composers’ and works’ stage success factors. • Further, four success-related ideal types of orchestral repertoire were identified. • Domestic works are preferred in modern repertoire and US orchestras are more diverse and innovative. Classical music orchestras are vital to the cultural scenes of both Germany and USA. Despite ongoing discussions on musical canon, gender equality, and repertoire innovation, empirical studies on the actual frequency of performances of individual classical music works in both countries are scarce. In this study, concert programs of professional orchestras from the 2019/20 and 2023/24 seasons were collected via web scraping and enriched with metadata from the various online sources using data linkage. In addition to a detailed descriptive statistical analysis, we determined key factors of stage performance frequency using random forest models. Based on these factors, canonical repertoire types were then identified using Latent Class Analysis. Internal factors for stage success of individual works from these repertoires were subsequently determined using Mixture Regression. Results suggest that normative criteria tend to play a more decisive role for program selection in Germany, while the USA lean more towards popular criteria. Four repertoire types are distinguished, primarily based on work and artist prestige, work age and work duration. Generally, programming in the US appears to be more diverse and innovative, and a pre- and post-pandemic comparison of the combined program data from the two countries reveals little differences. These empirical results support and expand on previous musicological studies of canon and can serve as orientation knowledge for cultural policy. Furthermore, the available dataset can also be used by future studies to examine the ways of curating classical concert events and for more fine-grained sub-repertoire analyses.