Automatic identification of musical schemata

This study was stimulated by the Galant musical schemata theory (GMST), an example–based learning and compositional practice that peaked in popularity around the early 18th century in Europe, suggesting a culturally–defined classification of polyphonic patterns. Under the premises of the GMST and by relating notions from psychology towards a cognitive model for musical schemata identification, an explanatory system based on music-analytical thought–patterns was examined, aiming to describe the mental processes involved in three accumulative operations: a) the schematic analysis of music notation into a stream of salient musical elements and, eventually, GMST–related musical structures, providing the standard form of music notation interpretation for the examined model; b) the example–based learning of musical schemata definitions from annotated examples, and c) the discovery of – similar to the Galant – musical schemata family–types in corpora. The proposed music–analytical model was tested with a novel computational system performing three tasks accordingly: i) search, matching representations of Galant musical schemata prototypes and examining similarity models; ii) classification, classifying segments of schematic analysis according to musical schemata family–type definitions that are extracted and maintained utilising annotated examples and pattern detection methods, and iii) polyphonic pattern extraction, examining methods that form and categorise musical schemata structures. The proposed model was evaluated employing the technological research methodology, and computational experiments quantified the performance of the computational system implementing the aforementioned tasks by utilising Galant musical schemata–annotated datasets and task–oriented performance metrics. Results show a functional cognitive model for complex music–analytical operations with polyphonic patterns, suggesting methodological explanations as to how these may be addressed by the initiate. Based on the foundations established in this project, it may in the future become possible to develop computational tools that have applications in music education and musicological research.

Name dc:type.qualificationname

PhD

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

De Montfort University

Year dc:date.issued

2022

Author dc:creator

• Katsiavalos, Andreas

Licence

• https://dora.dmu.ac.uk/bitstreams/f74b6073-87ac-4faa-a27a-b2a942e7fb06/download