PRIISM Seminar: Mark Glickman - Data Tripper: Authorship Attribution Analysis of Lennon-McCartney Songs

PRIISM and Stern's Department of Technology, Operations, and Statistics would like to invite you to a co-sponsored event to usher in the new semester. The afternoon will start with a lecture (3:00 - 4:15) that mixes music with data science featuring Mark Glickman from Harvard.

The talk will be followed by a Statistics at NYU Mixer (4:15-5:30), where food and beverages will be provided and you will have an opportunity to socialize with statisticians and other data scientists and empirical researchers from a variety of departments.

Please join us for this fun and informative event!

Bio

Dr. Glickman is a Senior Lecturer in Statistics at Harvard University, a Fellow of the American Statistical Association (ASA), and a Senior Statistician at the Center for Healthcare Organization and Implementation Research. His research interests are primarily in the areas of statistical modeling for rating competitors in games and sports, and in statistical methods applied to problems in health services research. Besides publishing extensively in these areas, Dr. Glickman invented the Glicko and Glicko-2 rating systems (and is a U.S. national master in Chess), and has served as Chair and Program Chair of the ASA's Section on Statistics in Sports.

Abstract

The songwriting duo of John Lennon and Paul McCartney, the two founding members of the Beatles, have composed some of the most popular and memorable songs of the last century. Despite having authored songs under the joint credit agreement of Lennon-McCartney, it is well-documented that most of their songs or portions of songs were primarily written by exactly one of the two. Some Lennon-McCartney songs are actually of disputed authorship.

For Lennon-McCartney songs of known and unknown authorship written and recorded over the period 1962-66, we extracted musical features from each song or song portion. These features consist of the occurrence of melodic notes, chords, melodic note pairs, chord change pairs, and four-note melody contours. We developed a prediction model based on variable screening followed by logistic regression with elastic net regularization. We applied our model to the prediction of songs and song portions with unknown or disputed authorship.