Analyzing Popular Songs Through Statistical Embeddings

Music City Center 

Statistical modeling of popular music presents a unique challenge due to the complexity of song structures, which cannot be easily analyzed using conventional statistical tools. However, recent advances in data science have shown that converting non-standard data objects into real vector-valued embeddings enables meaningful statistical analysis. In this talk, we demonstrate an approach based on logistic principal component analysis (logistic PCA; Landgraf and Lee, 2015) to construct embeddings from global song features, allowing for standard multivariate analysis. We apply this method to a corpus of Lennon and McCartney songs from 1962–1966, using embeddings derived from chords, pitch transitions, and melodic contours. Our analysis explores how these song embeddings cluster by Beatles album, how songwriting styles evolved over time, and whether Lennon and McCartney's compositions exhibited convergence or divergence. This embedding-based approach offers a powerful framework for statistically examining musical structure and stylistic development in popular music.

Lennon and McCartney

Song structure

Logistic PCA

Popular music

Feature representation