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

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

WoS Article

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, e.g., 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.

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, e.g., 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.

classification; interpretation; machine learning; music analysis; music information retrieval; origin; string quartet; synchronization

classification; interpretation; machine learning; music analysis; music information retrieval; origin; string quartet; synchronization

IŠTVÁNEK, M.; MIKLÁNEK, Š.; SPURNÝ, L.

2024

11.03.2023

MDPI

2076-3417

Applied Sciences - Basel

13

6

Swiss Confederation

1

20

20

https://www.mdpi.com/2076-3417/13/6/3603

http://hdl.handle.net/11012/213557

applsci-13-03603-v2