Automatic recognition of texture in Renaissance Music. Parada-cabaleiro, E., Schmitt, M., Batliner, A., Schuller, B., & Schedl, M. In Proceedings of 22nd International Society for Music Information Retrieval Conference, pages 509–516, 2021. abstract bibtex Renaissance music constitutes a resource of immense rich- ness for Western culture, as shown by its central role in digital humanities. Yet, despite the advance of computa- tional musicology in analysing other Western repertoires, the use of computer-based methods to automatically re- trieve 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 out- comes indicate that Low Level Descriptors, such as inter- vals, can successfully convey differences in High Level features, such as texture. Furthermore, our baseline re- sults, particularly the ones from a Convolutional Neural Network, show that machine learning can be successfully used to automatically identify sections in madrigals asso- ciated with specific textures from symbolic sources. 1.
@InProceedings{ parada-cabaleiro.ea2021-automatic,
author = {Parada-cabaleiro, Emilia and Schmitt, Maximilian and
Batliner, Anton and Schuller, Bj{\"{o}}rn and Schedl,
Markus},
year = {2021},
title = {Automatic recognition of texture in Renaissance Music},
abstract = {Renaissance music constitutes a resource of immense rich-
ness for Western culture, as shown by its central role in
digital humanities. Yet, despite the advance of computa-
tional musicology in analysing other Western repertoires,
the use of computer-based methods to automatically re-
trieve 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 out-
comes indicate that Low Level Descriptors, such as inter-
vals, can successfully convey differences in High Level
features, such as texture. Furthermore, our baseline re-
sults, particularly the ones from a Convolutional Neural
Network, show that machine learning can be successfully
used to automatically identify sections in madrigals asso-
ciated with specific textures from symbolic sources. 1.},
booktitle = {Proceedings of 22nd International Society for Music
Information Retrieval Conference},
keywords = {computational musicology},
mendeley-tags= {computational musicology},
pages = {509--516}
}
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