A Clustering Analysis of Renaissance Polyphony Using State-Space Models

نویسندگان

چکیده

In this paper, we propose a novel clustering method to represent and compare works of symbolic music, in particular the music database Josquin Research Project. We also study results our methods on as whole, well for some specific cases. The that revolve around modelling activity twelve pitch classes (i.e., chromae) over time. suggest by activity, can capture harmonic progressions music. By converting models vector representation, analyse dataset using techniques from machine learning. These be used group, visualize, classify corpus. This way, attributions insecurely attributed pieces dataset, based solely contained songs. apply final classification unica Leuven Chansonnier, give first suggestions attribution these world computational musicology. then existing analyzing find is slightly worse at identifying composers Renaissance works. result was not completely unexpected, features extract are only musical present Finally, use best representation devised together with unsupervised dimensionality reduction algorithms create scatterplot visualization entire Project dataset.

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ژورنال

عنوان ژورنال: Journal of the Alamire Foundation

سال: 2021

ISSN: ['2032-5371', '2507-0320']

DOI: https://doi.org/10.1484/j.jaf.5.124209