MusicLynx: Exploring Music Through Artist Similarity Graphs

Abstract

MusicLynx is a web application for music discovery that enables users to explore an artist similarity graph constructed by linking together various open public data sources. It provides a multifaceted browsing platform that strives for an alternative, graph-based representation of artist connections to the grid-like conventions of traditional recommendation systems. Bipartite graph filtering of the Linked Data cloud, content-based music information retrieval, machine learning on crowd-sourced information and Semantic Web technologies are combined to analyze existing and create new categories of music artists through which they are connected. The categories can uncover similarities between artists who otherwise may not be immediately associated: for example, they may share ethnic background or nationality, common musical style or be signed to the same record label, come from the same geographic origin, share a fate or an affliction, or have made similar lifestyle choices. They may also prefer similar musical keys, instrumentation, rhythmic attributes, or even moods their music evokes. This demonstration is primarily meant to showcase the graph-based artist discovery interface of MusicLynx: how artists are connected through various categories, how the different graph filtering methods affect the topology and geometry of linked artists graphs, and ways in which users can connect to external services for additional content and information about objects of their interest.