In-depth analysis of music structure as a text network

Abstract

Music, enchanting and poetic, permeates every corner of human civilization. Although music is not unfamiliar to people, our understanding of its essence remains limited, and there is still no universally accepted scientific description. This is primarily due to music being regarded as a product of reason and emotion, making it difficult to define. This article treats musical texts as a complex system. This view echoes linguist John Rupert Firth's insight that understanding a word involves defining it through its surrounding relationships. To construct the network we first build a linear regression model with threshold values to assign conditions to the links among note, time, and volume. Then a clustering coefficient representing regional characteristics is utilized to define the word. Finally, the statistical distribution of the text is strictly required to adhere to the grammatical properties of statistical linguistics, such as Zipf's law, to adjust the weights of the linear regression model and achieve optimal results. These processes enable us to comprehend the structural differences in music across different periods with scientific rigor. Relying on the advantages of structuralism, we concentrate on the relationships and order between the physical elements of music, rather than getting entangled in the blurred boundaries of science and philosophy. Aside from serving as a bridge connecting music to natural language processing and knowledge graphs, the technical methods developed in this work offer a more intuitive approach to elucidate the relationships among elements of a complex network.