A Dataset of Symbolic Texture Annotations in Mozart Piano Sonatas
| Autor: | Couturier, Louis, Bigo, Louis, Levé, Florence |
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| Přispěvatelé: | Modélisation, Information et Systèmes - UR UPJV 4290 (MIS), Université de Picardie Jules Verne (UPJV), Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 (CRIStAL), Centrale Lille-Université de Lille-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | International Society for Music Information Retrieval Conference (ISMIR 2022) International Society for Music Information Retrieval Conference (ISMIR 2022), Dec 2022, Bengaluru, India. ⟨10.5281/zenodo.7316712⟩ |
| DOI: | 10.5281/zenodo.7316712 |
| Popis: | Musical scores are generally analyzed under different aspects, notably melody, harmony, rhythm, but also through their texture, although this last concept is arguably more delicate to formalize. Symbolic texture depicts how sounding components are organized in the score. It outlines the density of elements, their heterogeneity, role and interactions. In this paper, we release a set of manual annotations for each bar of 9 movements among early piano sonatas by W. A. Mozart, totaling 1164 labels that follow a syntax dedicated to piano score texture. A quantitative analysis of the annotations highlights some characteristic textural features in the corpus. In addition, we present and release the implementation of low-level descriptors of symbolic texture. These descriptors can be correlated with texture annotations and used in different machine-learning tasks. Along with provided data, they offer promising applications in computer assisted music analysis and composition. |
| Databáze: | OpenAIRE |
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