The role of musical influence has long been debated by scholars and critics in the humanities, but never in a data-driven way. In this work we approach the question of influence by applying topic-modeling tools (Blei & Lafferty, 2006; Gerrish & Blei, 2010) to a dataset of 24941 songs by 9222 artists, from the years 1922 to 2010. We find the models to be significantly correlated with a human-curated influence measure, and to clearly outperform a baseline method. Further using the learned model to study properties of influence, we find that musical influence and musical innovation are not monotonically correlated. However, we do find that the most influential songs were more innovative during two time periods: the early 1970’s and the mid 1990’s.
|Title of host publication||Proceedings of the 30th International Conference on Machine Learning|
|Editors||Sanjoy Dasgupta, David McAllester|
|Place of Publication||Atlanta, Georgia, USA|
|Number of pages||9|
|State||Published - 1 May 2013|
|Name||Proceedings of Machine Learning Research|