Linked data collection and analysis platform for music information retrieval

Abstract

There has been extensive research on music information retrieval (MIR), such as signal processing, pattern mining, and information retrieval. In such studies, audio features extracted from music are commonly used, but there is no open platform for data collection and analysis of audio features. Therefore, we build the platform for the data collection and analysis for MIR research. On the platform, we represent the music data with Linked Data, which are in a format suitable for computer processing, and also link data fragments to each other. By adopting the Linked Data, the music data will become easier to publish and share, and there is an advantage that complex music analysis will be facilitated. In this paper, we first investigate the frequency of the audio features used in previous studies on MIR for designing the Linked Data schema. Then, we build a platform, that automatically extracts the audio features and music metadata from YouTube URIs designated by users, and adds them to our Linked Data DB. Finally, the sample queries for music analysis and the current record of music registrations in the DB are presented.