November 7, 2021 Conference paper Open Access

Emilia Parada-Cabaleiro; Maximilian Schmitt; Anton Batliner; Bjorn W. Schuller; Markus Schedl

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    <subfield code="a">Emilia Parada-Cabaleiro</subfield>
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    <subfield code="a">Automatic Recognition of Texture in Renaissance Music</subfield>
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    <subfield code="a">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.</subfield>
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