This project focuses on systems for the automatic description of digital music and analysis systems that can be based on those descriptions. The rapid growth of digital media delivery in recent years has lead to an increase in the demand for tools and techniques for managing huge music catalogues.

This growth began with peer-to-peer file sharing services, internet radio stations, such as the Shoutcast network, and online music purchase services such as Apple's iTunes music store. Recently, these services have been joined by a host of music subscription services, which allow unlimited access to very large music catalogues, backed by digital media companies or record labels, including offerings from Yahoo, RealNetworks (Rhapsody), BTOpenworld, AOL, MSN, Napster,, Streamwaves, and Emusic. By the end of 2006, worldwide online music delivery is expected to be a $2 billion market.

All online music delivery services share the challenge of providing the right content to each user. A music purchase service will only be able to make sales if it can consistently match users to the content that they are looking for, and users will only remain members of music subscription services while they can find new music that they like. Owing to the size of the music catalogues in use, the existing methods of organising, browsing and describing online music collections are unlikely to be sufficient for this task. In order to implement intelligent song suggestion, playlist generation and audio content-based search systems for these services, efficient and accurate systems for estimating the similarity of two pieces of music will need to be defined.

Work at UEA on Music Information Retrieval (MIR) has focused on systems for estimating the similarity of both audio and symbolic (midi) music examples and the automated extraction of metadata (such as a Genre, artist or mood labels) from them. Systems for genre classification (audio and symbolic) and artist identification were submitted to both the ISMIR2004 Audio description contest and the 1st annual Music Information Evaluation eXchange (MIREX) and achieved very good results. Researchers from UEA were involved in the definition, organisation and execution of the 1st MIREX competition.

Researchers from UEA have also been heavily involved in the development of the Music-2-Knowledge (M2K) toolkit for Music Information Retrieval (MIR) researchers.


Research Team

Prof. Stephen Cox, Mr. Kris West