Verifying tag annotation and performing genre classification in music data via association analysis
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Date
2014
Authors
Arjannikov, Tom
Journal Title
Journal ISSN
Volume Title
Publisher
Lethbridge, Alta. : University of Lethbridge, Dept. of Mathematics and Computer Science
Abstract
Music Information Retrieval aims to automate the access to large-volume music data, including browsing, retrieval, storage, etc. The work presented in this thesis tackles two non-trivial problems in the field.
First problem deals with music tags, which provide descriptive and rich information about a music piece, including its genre, artist, emotion, instrument, etc. At present, tag annotation is largely a manual process, which often results in tags that are subjective, ambiguous, and error-prone. We propose a novel approach to verify the quality of tag annotation in a music dataset through association analysis.
Second, we employ association analysis to predict music genres based on features extracted directly from music. We build an association-based classifier, which finds inherent associations between music features and genres.
We demonstrate the effectiveness of our approaches through a series of simulations and experiments using various benchmark music datasets.
Description
Keywords
data mining , machine learning , information retrieval , music information retrieval