A Bayesian Approach to Understanding Music Popularity
Abstract
The Billboard Hot 100 has been the main record chart for popular music in the American
music industry since its first official release in 1958. Today, this rank- ing is
based upon the frequency of which a song is played on the radio, streamed online,
and its sales. Over time, however, the limitations of the chart have become more pronounced
and record labels have tried different strategies to maximize a song’s potential on
the chart in order to increase sales and success. This paper intends to analyze metadata
and audio analysis features from a random sample of one million popular tracks, dating
back to 1922, and assess their potential on the Billboard Hot 100 list. We compare
the results of Bayesian Additive Regression Trees (BART) to other decision tree methods
for predictive accuracy. Through the use of such trees, we can determine the interaction
and importance of differ- ent variables over time and their effects on a single’s
success on the Billboard chart. With such knowledge, we can assess and identify past
music trends, and provide producers with the steps to create the ‘perfect’ commercially
successful song, ultimately removing the creative artistry from music making.
Type
Honors thesisDepartment
Statistical SciencePermalink
https://hdl.handle.net/10161/9747Citation
Shapiro, Heather (2015). A Bayesian Approach to Understanding Music Popularity. Honors thesis, Duke University. Retrieved from https://hdl.handle.net/10161/9747.Collections
More Info
Show full item record
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.
Rights for Collection: Undergraduate Honors Theses and Student papers
Works are deposited here by their authors, and represent their research and opinions, not that of Duke University. Some materials and descriptions may include offensive content. More info