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Paper details
Number 4 - December 2020
Volume 30 - 2020
AI based algorithms for the detection of (ir)regularity in musical structure
Lorena Mihelač, Janez Povh
Abstract
Regularity in musical structure is experienced as a strongly structured texture with repeated and periodic patterns, with the
musical ideas presented in an appreciable shape to the human mind. We recently showed that manipulation of musical
content (i.e., deviation of musical structure) affects the perception of music. These deviations were detected by musical
experts, and the musical pieces containing them were labelled as irregular. In this study, we replace the human expert
involved in detection of (ir)regularity with artificial intelligence algorithms. We evaluated eight variables measuring entropy
and information content, which can be analysed for each musical piece using the computational model called Information
Dynamics of Music and different viewpoints. The algorithm was tested using 160 musical excerpts. A preliminary statistical
analysis indicated that three of the eight variables were significant predictors of regularity (E_cpitch, IC_cpintfref, and E_cpintfref). Additionally, we observed linear separation between regular and irregular excerpts; therefore, we employed support vector machine and artificial neural network (ANN) algorithms with a linear kernel and a linear activation function, respectively, to predict regularity. The final algorithms were capable of predicting regularity with an accuracy ranging from 89% for the ANN algorithm using only the most significant predictor to 100% for the ANN algorithm using all eight prediction variables.
Keywords
regularity, musical structure, perception, AI algorithms