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@article{2268037,
author = {Spurný, Lubomír and Ištvánek, Matěj and Miklánek, Štěpán},
article_location = {BASEL},
article_number = {6},
doi = {http://dx.doi.org/10.3390/app13063603},
keywords = {Czech music; string quartet; music analysis; classification; interpretation; machine learning; music information retrieval; origin; synchronization},
language = {eng},
issn = {2076-3417},
journal = {Applied Sciences-Basel},
title = {Classification of Interpretation Differences in String Quartets Based on the Origin of Performers},
url = {https://www.mdpi.com/2076-3417/13/6/3603},
volume = {13},
year = {2023}
}
TY - JOUR
ID - 2268037
AU - Spurný, Lubomír - Ištvánek, Matěj - Miklánek, Štěpán
PY - 2023
TI - Classification of Interpretation Differences in String Quartets Based on the Origin of Performers
JF - Applied Sciences-Basel
VL - 13
IS - 6
SP - 1-20
EP - 1-20
PB - MDPI AG
SN - 20763417
KW - Czech music
KW - string quartet
KW - music analysis
KW - classification
KW - interpretation
KW - machine learning
KW - music information retrieval
KW - origin
KW - synchronization
UR - https://www.mdpi.com/2076-3417/13/6/3603
N2 - 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.
ER -
SPURNÝ, Lubomír, Matěj IŠTVÁNEK a Štěpán MIKLÁNEK. Classification of Interpretation Differences in String Quartets Based on the Origin of Performers. \textit{Applied Sciences-Basel}. BASEL: MDPI AG, 2023, roč.~13, č.~6, s.~1-20. ISSN~2076-3417. Dostupné z: https://dx.doi.org/10.3390/app13063603.
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