In Search of the Horowitz Factor


  • Gerhard Widmer
  • Simon Dixon
  • Werner Goebl
  • Elias Pampalk
  • Asmir Tobudic



The article introduces the reader to a large interdisciplinary research project whose goal is to use AI to gain new insight into a complex artistic phenomenon. We study fundamental principles of expressive music performance by measuring performance aspects in large numbers of recordings by highly skilled musicians (concert pianists) and analyzing the data with state-of-the-art methods from areas such as machine learning, data mining, and data visualization. The article first introduces the general research questions that guide the project and then summarizes some of the most important results achieved to date, with an emphasis on the most recent and still rather speculative work. A broad view of the discovery process is given, from data acquisition through data visualization to inductive model building and pattern discovery, and it turns out that AI plays an important role in all stages of such an ambitious enterprise. Our current results show that it is possible for machines to make novel and interesting discoveries even in a domain such as music and that even if we might never find the "Horowitz Factor," AI can give us completely new insights into complex artistic behavior.




How to Cite

Widmer, G., Dixon, S., Goebl, W., Pampalk, E., & Tobudic, A. (2003). In Search of the Horowitz Factor. AI Magazine, 24(3), 111.