Come Together: Analyzing Popular Songs Through Statistical Embeddings

TL;DR

This paper introduces a statistical embedding method using logistic PCA to analyze complex musical structures in popular songs, revealing stylistic trends and evolution over time.

Contribution

It presents a novel embedding approach for musical data, enabling standard statistical analysis of song structures and stylistic changes in popular music.

Findings

Embeddings cluster by Beatles album and reveal stylistic evolution.

Songwriting styles of Lennon and McCartney show convergence and divergence.

The method provides a new framework for analyzing musical structure statistically.

Abstract

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 work, we demonstrate an approach based on logistic principal component analysis 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, melodic notes, chord and 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.