Classification of Interpretation Differences in String Quartets Based on the Origin of Performers

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Year of publication 2023
Type Article in Periodical
Magazine / Source Applied Sciences-Basel
MU Faculty or unit

Faculty of Arts

Web Web výsledku
Keywords Czech music; string quartet; music analysis; classification; interpretation; machine learning; music information retrieval; origin; synchronization
Description Music Information Retrieval aims at extracting relevant features from music material, while Music Performance Analysis uses these features to perform semi-automated music analysis. Examples of interdisciplinary cooperation are, for example, various classification tasks—from recognizing specific performances, musical structures, and composers to identifying music genres. However, some classification problems have not been addressed yet. In this paper, we focus on classifying string quartet music interpretations based on the origin of performers. Our dataset consists of string quartets from composers A. Dvořák, L. Janáček, and B. Smetana. After transferring timing information from reference recordings to all target recordings, we apply feature selection methods to rank the significance of features. As the main contribution, we show that there are indeed origin-based tempo differences, distinguishable by measure durations, by which performances may be identified. Furthermore, we train a machine learning classifier to predict the performers’ origin. We evaluate three different experimental scenarios and achieve higher classification accuracy compared to the baseline using synchronized measure positions.
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