Abstract
Fugue analysis is a challenging problem. We propose an algorithm that detects subjects and counter-subjects in a symbolic score where all the voices are separated, determining the precise ends and the occurrence positions of these patterns. The algorithm is based on a diatonic similarity between pitch intervals combined with a strict length matching for all notes, except for the first and the last one. On the 24 fugues of the first book of Bach’s Well-Tempered Clavier, the algorithm predicts 66% of the subjects with a musically relevant end, and finally retrieves 85% of the subject occurrences, with almost no false positive.
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Giraud, M., Groult, R., Levé, F. (2013). Subject and Counter-Subject Detection for Analysis of the Well-Tempered Clavier Fugues. In: Aramaki, M., Barthet, M., Kronland-Martinet, R., Ystad, S. (eds) From Sounds to Music and Emotions. CMMR 2012. Lecture Notes in Computer Science, vol 7900. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41248-6_24
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DOI: https://doi.org/10.1007/978-3-642-41248-6_24
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