Compositemap: A Novel Music Similarity Measure for Personalized Multimodal Music Search

Publication Type

Conference Proceeding Article

Publication Date

2009

Abstract

How to measure and model the similarity between different music items is one of the most fundamental yet challenging research problems in music information retrieval. This paper demonstrates a novel multimodal and adaptive music similarity measure (CompositeMap) with its application in a personalized multimodal music search system. CompositeMap can effectively combine music properties from different aspects into compact signatures via supervised learning, which lays the foundation for effective and efficient music search. In addition, an incremental Locality Sensitive Hashing algorithm is developed to support more efficient search processes. Experimental results based on two large music collections reveal various advantages in effectiveness, efficiency, adaptiveness, and scalability of the proposed music similarity measure and the music search system.

Keywords

Music, Similarity Measure, Personalization, Search

Discipline

Communication Technology and New Media | Computer Sciences

Research Areas

Information Systems and Management

Publication

17th ACM International Conference on Multimedia (ACM Multimedia), System Demo. Track

First Page

973

Last Page

974

ISBN

9781605586083

Identifier

10.1145/1631272.1631474

Publisher

ACM

City or Country

New York

Additional URL

http://dx.doi.org/10.1145/1631272.1631474

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