This chapter presents music indexing schemes by exploiting and utilizing
two data sources: surrounding text on web pages and metedata describing music
attributes. We first present a framework to index and organize web music by discovering
its inherent structure attributes with trustworthy domain knowledge. In this
approach, layered LSI spaces are first built to represent the hierarchically structured
domain knowledge, music object representation is then constructed through hyperlink
analysis, and the structure attributes of a music object is finally discovered by
matching against the domain knowledge. This approach also indicates a new way to
organize dispersive information on the Surface Web by using trustworthy Deep Web
knowledge. We further present our work on enhancing music indexing with automatic
music annotation, which attempts to automatically annotate a music object
with a set of semantic labels (tags) to create metadata describing its attributes and
to facilitate music search, organization, and recommendation. Besides modeling
music annotation as a multi-label binary classification task, we also attempt to discover
the correlation between semantic labels and present an approach to collective
music semantic annotation.
Keywords: Music indexing, music search, automatic music annotation, collective annotation, deep web, knowledge space building, layered LSI, web object, web object
representation, confidence propagation, multi-label classification
