On the Modeling of Musical Solos as Complex Networks
This work addresses the problem of analyzing and modeling musical structures for researchers and developers in audio and multimedia applications, but it is incremental as it builds on existing network theory approaches.
The authors tackled the problem of modeling musical solos by representing melodies as directed networks where notes are nodes and sequences are edges, and they applied complex network metrics to analyze a set of guitar solos, showing that this model can impact applications like music classification and generation.
Notes in a musical piece are building blocks employed in non-random ways to create melodies. It is the "interaction" among a limited amount of notes that allows constructing the variety of musical compositions that have been written in centuries and within different cultures. Networks are a modeling tool that is commonly employed to represent a set of entities interacting in some way. Thus, notes composing a melody can be seen as nodes of a network that are connected whenever these are played in sequence. The outcome of such a process results in a directed graph. By using complex network theory, some main metrics of musical graphs can be measured, which characterize the related musical pieces. In this paper, we define a framework to represent melodies as networks. Then, we provide an analysis on a set of guitar solos performed by main musicians. Results of this study indicate that the presented model can have an impact on audio and multimedia applications such as music classification, identification, e-learning, automatic music generation, multimedia entertainment.