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Towards an Automated System for Music Event Detection

Authors:
Jian Xi
Michael Spranger
Hanna Siewerts
Dirk Labudde

Keywords: Event Detection; Text Classification; Categorization; Named Entity Recognition

Abstract:
Announcements of events are regularly spread using the Internet, e.g., via online newspapers or social media. Often, these events involve playing music publicly that is protected by international copyright laws. Authorities entrusted with the protection of the artists’ interests have to find unregistered music events in order to fully exercise their duty. As a requirement, they need to find texts in the Internet that are related to such events like announcements or reports. However, event detection is a challenging task in the field of Text Mining due to the enormous variety of information that needs to be considered and the large amount of data that needs to be processed. Because no benchmark data is available for the domain of music event detection, in this paper a gold standard dataset is presented and made publicly available for further development and improvement. Subsequently, a process chain for the detection of music events incorporating external knowledge is proposed. Finally, the performance of three classification models is compared using various feature sets and two different datasets. The best performances reach an F1-measure of 0.94 and 0.946 for the classification of music and music event relevance, respectively.

Pages: 22 to 27

Copyright: Copyright (c) IARIA, 2018

Publication date: November 18, 2018

Published in: conference

ISSN: 2308-4510

ISBN: 978-1-61208-678-1

Location: Athens, Greece

Dates: from November 18, 2018 to November 22, 2018