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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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