A Multi-objective Evolutionary Approach to Identify Relevant Audio Features for Music Segmentation
Vatolkin I, Koch M, Müller M (2021)
Publication Type: Book chapter / Article in edited volumes
Publication year: 2021
Journal
Publisher: Springer Cham
Edited Volumes: Artificial Intelligence in Music, Sound, Art and Design
Pages Range: 327-343
ISBN: 9783030729134
DOI: 10.1007/978-3-030-72914-1_22
Abstract
The goal of automatic music segmentation is to calculate boundaries between musical parts or sections that are perceived as semantic entities. Such sections are often characterized by specific musical properties such as instrumentation, dynamics, tempo, or rhythm. Recent data-driven approaches often phrase music segmentation as a binary classification problem, where musical cues for identifying boundaries are learned implicitly. Complementary to such methods, we present in this paper an approach for identifying relevant audio features that explain the presence of musical boundaries. In particular, we describe a multi-objective evolutionary feature selection strategy, which simultaneously optimizes two objectives. In a first setting, we reduce the number of features while maximizing an F-measure. In a second setting, we jointly maximize precision and recall values. Furthermore, we present extensive experiments based on six different feature sets covering different musical aspects. We show that feature selection allows for reducing the overall dimensionality while increasing the segmentation quality compared to full feature sets, with timbre-related features performing best.
Authors with CRIS profile
Involved external institutions
Germany (DE)How to cite
APA:
Vatolkin, I., Koch, M., & Müller, M. (2021). A Multi-objective Evolutionary Approach to Identify Relevant Audio Features for Music Segmentation. In Juan Romero, Tiago Martins, Nereida Rodríguez-Fernández (Eds.), Artificial Intelligence in Music, Sound, Art and Design. (pp. 327-343). Springer Cham.
MLA:
Vatolkin, Igor, Marcel Koch, and Meinard Müller. "A Multi-objective Evolutionary Approach to Identify Relevant Audio Features for Music Segmentation." Artificial Intelligence in Music, Sound, Art and Design. Ed. Juan Romero, Tiago Martins, Nereida Rodríguez-Fernández, Springer Cham, 2021. 327-343.
BibTeX: Download