Classification of Interpretation Differences in String Quartets Based on the Origin of Performers
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 perf...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/13/6/3603 |
| _version_ | 1797613683092750336 |
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| author | Matej Istvanek Stepan Miklanek Lubomir Spurny |
| author_facet | Matej Istvanek Stepan Miklanek Lubomir Spurny |
| author_sort | Matej Istvanek |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-11T06:58:17Z |
| format | Article |
| id | doaj.art-2077b19df9df454bae66030b09c031fd |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T06:58:17Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2077b19df9df454bae66030b09c031fd2023-11-17T09:24:07ZengMDPI AGApplied Sciences2076-34172023-03-01136360310.3390/app13063603Classification of Interpretation Differences in String Quartets Based on the Origin of PerformersMatej Istvanek0Stepan Miklanek1Lubomir Spurny2Department of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 61600 Brno, Czech RepublicDepartment of Telecommunications, Faculty of Electrical Engineering and Communication, Brno University of Technology, Technicka 12, 61600 Brno, Czech RepublicDepartment of Musicology, Faculty of Arts, Masaryk University, Janackovo Namesti 2a, 60200 Brno, Czech RepublicMusic 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.https://www.mdpi.com/2076-3417/13/6/3603classificationinterpretationmachine learningmusic analysismusic information retrievalorigin |
| spellingShingle | Matej Istvanek Stepan Miklanek Lubomir Spurny Classification of Interpretation Differences in String Quartets Based on the Origin of Performers Applied Sciences classification interpretation machine learning music analysis music information retrieval origin |
| title | Classification of Interpretation Differences in String Quartets Based on the Origin of Performers |
| title_full | Classification of Interpretation Differences in String Quartets Based on the Origin of Performers |
| title_fullStr | Classification of Interpretation Differences in String Quartets Based on the Origin of Performers |
| title_full_unstemmed | Classification of Interpretation Differences in String Quartets Based on the Origin of Performers |
| title_short | Classification of Interpretation Differences in String Quartets Based on the Origin of Performers |
| title_sort | classification of interpretation differences in string quartets based on the origin of performers |
| topic | classification interpretation machine learning music analysis music information retrieval origin |
| url | https://www.mdpi.com/2076-3417/13/6/3603 |
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