A Model-Based Approach to Constructing Music Similarity Functions

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Research Article A Model-Based Approach to Constructing Music Similarity Functions Kris West1 and Paul Lamere2 1 School 2 Sun

of Computer Sciences, University of East Anglia, Norwich NR4 7TJ, UK Microsystems Laboratories, Sun Microsystems, Inc., Burlington, MA 01803, USA

Received 1 December 2005; Revised 30 July 2006; Accepted 13 August 2006 Recommended by Ichiro Fujinaga Several authors have presented systems that estimate the audio similarity of two pieces of music through the calculation of a distance metric, such as the Euclidean distance, between spectral features calculated from the audio, related to the timbre or pitch of the signal. These features can be augmented with other, temporally or rhythmically based features such as zero-crossing rates, beat histograms, or fluctuation patterns to form a more well-rounded music similarity function. It is our contention that perceptual or cultural labels, such as the genre, style, or emotion of the music, are also very important features in the perception of music. These labels help to define complex regions of similarity within the available feature spaces. We demonstrate a machinelearning-based approach to the construction of a similarity metric, which uses this contextual information to project the calculated features into an intermediate space where a music similarity function that incorporates some of the cultural information may be calculated. Copyright © 2007 Hindawi Publishing Corporation. All rights reserved.

1.

INTRODUCTION

The rapid growth of digital media delivery in recent years has led 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, Listen.com, Streamwaves, and Emusic. By the end of 2006, worldwide online music delivery is expected to be a $2 billion market (http://blogs.zdnet.com/ITFacts/?p=9375). 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 organizing, 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