A Multi-objective Evolutionary Approach to Identify Relevant Audio Features for Music Segmentation
| Autor: | Meinard Müller, Marcel Koch, Igor Vatolkin |
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| Rok vydání: | 2021 |
| Předmět: |
Phrase
business.industry Computer science Feature selection 02 engineering and technology computer.software_genre Binary classification Dynamics (music) Feature (computer vision) 020204 information systems 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Segmentation Instrumentation (computer programming) Artificial intelligence Precision and recall business computer Natural language processing |
| Zdroj: | Artificial Intelligence in Music, Sound, Art and Design ISBN: 9783030729134 EvoMUSART |
| DOI: | 10.1007/978-3-030-72914-1_22 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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