Dodecaphonic Composer Identification Based On Complex Networks

In the musical composition process, even subconsciously, it is common for composers to imprint their personal signature implicitly within the work. This characteristic allows the recognition and the individualization of its origin through the sound assembly. With this in mind, the composer identification through the signature in his works allow us to classify a musical genre into more specific subcategories. However, the characteristics of that signature are of such great variation that make identification task difficult. This paper proposes the use of data mining within complex networks and machine learning techniques to classify a dodecaphonic musical work according to its composer. Considering the dodecaphonic matrix, two types of networks were generated: 1) intervals and 2) series. The feature vector is composed of new melodic topological measures adapted to the calculation from the adjacency matrix and conventional topological measurements. The classifiers Random Forest, AdaBoost and Random Subspace returned high values of accuracy and AUC (> 90%) in the identification of composers Schoenberg, Stravinsky and Webern. Confirming the existence of a relation among the characteristics of the original series, the selection and application of derived series and the composer. The results obtained revealed a good performance and showed that the experiment is very promising.