Modeling Musical Influence with Topic Models

Uri Shalit, Daphna Weinshall, Gal Chechik

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.
Original languageUndefined/Unknown
Title of host publicationProceedings of the 30th International Conference on Machine Learning
EditorsSanjoy Dasgupta, David McAllester
Place of PublicationAtlanta, Georgia, USA
Number of pages9
StatePublished - 1 May 2013

Publication series

NameProceedings of Machine Learning Research

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