Spectral Visibility Graphs: Application to Similarity of Harmonic Signals
This work addresses the problem of robust audio similarity analysis for researchers in signal processing, but it appears incremental as it adapts an existing visibility graph method to a new domain.
The authors tackled the problem of measuring similarity between harmonic audio signals by introducing spectral visibility graphs, which capture harmonic content and are resilient to broadband noise. They demonstrated its utility in experiments on real and synthetic audio data, though no concrete numbers were provided.
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.