Come Together: Analyzing Popular Songs Through Statistical Embeddings

2026-04-24

AI Summary

This study addresses the challenge of modeling the structural complexity of popular music using conventional statistical methods. It introduces logical principal component analysis (LPCA) into popular song research for the first time, constructing vector embeddings of Beatles songs composed by Lennon and McCartney between 1962 and 1966 based on chord and melodic features. By integrating multivariate clustering analysis, the approach enables standardized statistical treatment of unstructured musical data. The method not only reveals distinct clustering patterns of songs across albums but also quantitatively characterizes the stylistic evolution of the two composers and their mutual influences, offering a generalizable computational framework for music style analysis.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Matrix & Tensor Methods
• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• popular music
• statistical modeling
• song structure
• stylistic analysis
• music embeddings

Innovation

• statistical embeddings
• logistic PCA
• music analysis
• songwriting style
• multivariate analysis