Mining metadata from the web for AcousticBrainz

Abstract

Semantic annotations of music collections in digital libraries are important for organization and navigation of the collection. These annotations and their associated metadata are useful in many Music Information Retrieval tasks, and related fields in musicology. Music collections used in research are growing in size, and therefore it is useful to use semi-automatic means to obtain such annotations. We present software tools for mining metadata from the web for the purpose of annotating music collections. These tools expand on data present in the AcousticBrainz database, which contains software-generated analysis of music audio files. Using this tool we gather metadata and semantic information from a variety of sources including both community-based services such as MusicBrainz, Last.fm, and Discogs, and commercial databases including Itunes and AllMusic. The tool can be easily expanded to collect data from a new source, and is automatically updated when new items are added to AcousticBrainz. We extract genre annotations for recordings in AcousticBrainz using our tool and study the agreement between folksonomies and expert sources. We discuss the results and explore possibilities for future work.