Geometric Models for Musical Audio Data

Abstract

We study the geometry of sliding window embeddings of audio features that summarize perceptual information about audio, including its pitch and timbre.  These embeddings can be viewed as point clouds in high dimensions, and we add structure to the point clouds using a cover tree with adaptive thresholds based on multi-scale local principal component analysis to automatically assign points to clusters.  We connect neighboring clusters in a scaffolding graph, and we use knowledge of stratified space structure to refine our estimates of dimension in each cluster, demonstrating in our music applications that choruses and verses have higher dimensional structure, while transitions between them are lower dimensional. We showcase our technique with an interactive web-based application powered by Javascript and WebGL which plays music synchronized with a principal component analysis embedding of the point cloud down to 3D.  We also render the clusters and the scaffolding on top of this projection to visualize the transitions between different sections of the music.