Searching for Music Using Natural Language Queries and Relevance Feedback View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2008

AUTHORS

Peter Knees , Gerhard Widmer

ABSTRACT

We extend an approach to search inside large-scale music collections by enabling the user to give feedback on the retrieved music pieces. In the original approach, a search engine that can be queried through free-form natural language text is automatically built upon audio-based and Web-based similarity measures. Features for music pieces in the collection are derived automatically by retrieving relevant Web pages via Google queries and using the contents of these pages to construct term vectors. The additional use of information about acoustic similarity allows for reduction of the dimensionality of the vector space and characterization of audio pieces with no associated Web information. With the incorporation of relevance feedback, the retrieval of pieces can be adapted according to the preferences of the user and thus compensate for inadequately represented initial queries. The approach is evaluated on a collection comprising about 12,000 pieces by using semantic tags provided by Audioscrobbler and a user study which also gives further insights into users search behaviors. More... »

PAGES

109-121

Book

TITLE

Adaptive Multimedia Retrieval: Retrieval, User, and Semantics

ISBN

978-3-540-79859-0
978-3-540-79860-6

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-79860-6_9

DOI

http://dx.doi.org/10.1007/978-3-540-79860-6_9

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1037185897


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