Visualizing Music and Audio using Self-Similarity

Abstract

This paper presents a novel approach to visualizing
the time structure of music and audio. The
acoustic similarity between any two instants of
an audio recording is calculated and displayed
as a two-dimensional representation. Similar or
repeating elements are visually distinct, allowing
identification of structural and rhythmic
characteristics. Visualization examples are presented
for orchestral, jazz, and popular music.
Applications include content-based analysis
and segmentation, as well as tempo and structure
extraction.