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Adaptive Indexing for Semantic Music Information Retrieval

Chapter
Publication Date:
2010
abstract:
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
Iris type:
2.1 Contributo in volume (Capitolo o Saggio)
Keywords:
adaptive systems; multimedia; music; information retrieval
List of contributors:
Milani, Alfredo; Leung, Clement; Liu, Jiming; Chan, W. S. Alice
Authors of the University:
MILANI ALFREDO
Handle:
https://iris.unilink.it/handle/20.500.14085/43111
Book title:
Machine Learning Techniques for Adaptive Multimedia Retrieval: Technologies, Applications, and Perspectives
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URL

http://www.irma-international.org/viewtitle/49113/
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