# Spectral Visibility Graphs: Application to Similarity of Harmonic Signals

## TL;DR

This paper introduces spectral visibility graphs for audio signals, a novel method that captures harmonic content and noise resilience, enabling robust similarity measurement between harmonic signals in real and synthetic audio.

## Contribution

The paper proposes spectral visibility graphs and their degree as a new audio analysis tool, demonstrating their effectiveness in measuring harmonic signal similarity.

## Key findings

- Spectral visibility graph degree captures harmonic content effectively.
- The method is resilient to broadband noise.
- It enables robust similarity measurement in real and synthetic audio.

## Abstract

Graph theory is emerging as a new source of tools for time series analysis. One promising method is to transform a signal into its visibility graph, a representation which captures many interesting aspects of the signal. Here we introduce the visibility graph for audio spectra and propose a novel representation for audio analysis: the spectral visibility graph degree. Such representation inherently captures the harmonic content of the signal whilst being resilient to broadband noise. We present experiments demonstrating its utility to measure robust similarity between harmonic signals in real and synthesised audio data. The source code is available online.