AI based algorithms for the detection of (ir)regularity in musical structure

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.

How Do Artificial Neural Networks Classify Musical Triads? A Case Study in Eluding Bonini's Paradox

“…1). Many researchers use ANNs to study musical cognition (Bharucha, 1995(Bharucha, , 1999Dawson, 2018;Griffith, 1995;Griffith & Todd, 1999;Krumhansl et al, 2000;Krumhansl & Toiviainen, 2001;Mihelac & Povh, 2020;Russo, Vempala, & Sandstrom, 2013;Stevens & Latimer, 1992;Temperley, 2013;Todd & Loy, 1991). Such "musical networks," like most ANNs, face Bonini's paradox.…”