Adaptive Indexing for Semantic Music Information Retrieval

Adaptive Indexing for Semantic Music Information Retrieval

Clement H.C. Leung, Jiming Liu, Alfredo Milani, Alice W.S. Chan
Copyright: © 2011 |Pages: 14
DOI: 10.4018/978-1-61692-859-9.ch013
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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.
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Background

Music information retrieval (MIR) is a relatively young research area, emerged in the late 1990s (Crawford, 2005; Downie, 2003; Fingerhut, 2004), devoted to fulfill users’ music information needs (Orio, 2006). The ultimate task of a MIRS is the accurate transfer of musical information from a database to a user (Lesaffre, 2006). Since music can have different characteristics and its content can be represented in various ways and formats, it is not easy to deal with the retrieval problem in a large music database. In an effective “concept-based” multimedia retrieval system, efficient and meaningful indexing is necessary (Go’mez & Vicedo, 2007; Goth, 2004). Due to current technological limitations, it is impossible to extract the semantic content of music data objects automatically (Snoek et. al., 2006; Yang & Hurson, 2005). Meanwhile, the discovery and insertion of new indexing terms are always costly and time-consuming. Therefore, novel indexing mechanisms are required to support flexible music search and retrieval. Here, we present an innovative method which enables the retrieval of music information by a novel indexing approach.

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