Visibility graphs for robust harmonic similarity measures between audio spectra
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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. Such visibility graph captures the harmonic content whilst being resilient to broadband noise. We propose to use a structural distance between two graphs as a novel harmonic-biased similarity measure. We present experiments demonstrating the utility of this distance measure for real and synthesised audio data. The source code is available online.
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