Categorisation of polyphonic musical signals by using modularity community detection in audio-associated visibility network.

ABSTRACT: This article proposes a method to numerically characterise the homogeneity of polyphonic musical signals through community detection in audio-associated visibility networks and to detect patterns that allow the categorisation of these signals into two types of grouping based on this numerical characterization. To implement this methodology, we first calculate the variance fluctuation series in fixed-size windows of an audio stretch. Next we map this series onto a visibility graph, where the nodes are the points of the series, and the edges are defined by the visibility between each pair of points. Then, we measure the quality of the partitions of the network using the modularity and Louvain optimisation. We observed that a greater or lesser homogeneity of the magnitudes of the signal trans

SUBMITTER: Melo DFP 

PROVIDER: S-EPMC6214251 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature