Conference paper Open Access
Automatic Recognition of Texture in Renaissance Music
Emilia Parada-Cabaleiro; Maximilian Schmitt; Anton Batliner; Bjorn W. Schuller; Markus Schedl
Renaissance music constitutes a resource of immense richness for Western culture, as shown by its central role in digital humanities. Yet, despite the advance of computational musicology in analysing other Western repertoires, the use of computer-based methods to automatically retrieve relevant information from Renaissance music, e. g., identifying word-painting strategies such as madrigalisms, is still underdeveloped. To this end, we propose a score-based machine learning approach for the classification of texture in Italian madrigals of the 16th century. Our outcomes indicate that Low Level Descriptors, such as intervals, can successfully convey differences in High Level features, such as texture. Furthermore, our baseline results, particularly the ones from a Convolutional Neural Network, show that machine learning can be successfully used to automatically identify sections in madrigals associated with specific textures from symbolic sources.
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Indexed in
- Publication date:
- November 7, 2021
- DOI:
-
- Published in:
- Proceedings of the 22nd International Society for Music Information Retrieval Conference, ISMIR, Online, pp. 509-516.
- Meeting:
- International Society for Music Information Retrieval Conference (ISMIR 2021), Online, November 7-12, 2021
- Communities:
- License (for files):
- Creative Commons Attribution 4.0 International