Home // International Journal On Advances in Intelligent Systems, volume 13, numbers 1 and 2, 2020 // View article

Music Event Detection Leveraging Feature Selection based on Ant Colony Optimization

Authors:
Jian Xi
Michael Spranger
Dirk Labudde

Keywords: Event Detection; Text Classification; Named Entity Recognition; Feature Selection; Ant Colony Optimization

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. In this paper, a process chain for the detection of music events incorporating external knowledge is proposed. Furthermore, a feature selection algorithm based on ant colony optimization to find featurse with a high degree of explanatory power is presented. Finally, the performance of five different machine learningalgorithmsincludingtwolearningensemblesiscompared using various feature sets and two different datasets. The best performances reach an F1-measure of 0.95 for music texts and 0.968 for music event texts, respectively.

Pages: 36 to 47

Copyright: Copyright (c) to authors, 2020. Used with permission.

Publication date: June 30, 2020

Published in: journal

ISSN: 1942-2679