Adaptive indexing for semantic music information retrieval

Clement H C LEUNG, Jiming LIU, Alfredo Milani, Alice W.S. Chan

Research output: Chapter in book/report/conference proceedingChapterpeer-review


With the rapid advancement of music compression and storage technologies, digital music can be easily created, shared and distributed, not only in computers, but also in numerous portable digital devices. Music often constitutes a key component in many multimedia databases, and as they grow in size and complexity, their meaningful search and retrieval become important and necessary. Music Information Retrieval (MIR) is a relatively young and challenging research area started since the late 1990s. Although some form of music retrieval is available on the Internet, these tend to be inflexible and have significant limitations. Currently, most of these music retrieval systems only rely on low-level music information contents (e.g., metadata, album title, lyrics, etc.), and in this chapter, the authors present an adaptive indexing approach to search and discover music information. Experimental results show that through such an indexing architecture, high-level music semantics may be incorporated into search strategies.

Original languageEnglish
Title of host publicationMachine Learning Techniques for Adaptive Multimedia Retrieval
Subtitle of host publicationTechnologies, Applications, and Perspectives
PublisherIGI Global
Number of pages14
ISBN (Print)9781616928599
Publication statusPublished - 2010

Scopus Subject Areas

  • Computer Science(all)


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