Neural Network for Musical Data Mining for Phrase Boundary Detection

Daniel Henel, Aleksander Mazur, Marcin Retajczyk, Weronika T. Adrian, Krzysztof Kluza, Adrian Horzyk

Abstract: The surge of interest in artificial intelligence systems has sparked new directions of research in the realm of music data analysis. At the same time, the exploration and exploitation of intrinsic musical structures and sequences, a timeless endeavor, continue to captivate scholars and practitioners alike. In this context, the fusion of computational techniques with music analysis emerges as a natural progression. One of the pivotal crossroads in this convergence is the identification of musical phrase boundaries, pivotal demarcations that underpin the organizational fabric of a musical composition. This article pioneers an inventive approach to address the challenge of detecting these musical phrase boundaries, harnessing the power of artificial neural networks. However, the innovation does not stop there; the pinpointed phrase boundaries undergo a comprehensive dissection utilizing pattern mining techniques. The focus of this analysis is on unveiling recurrent motifs and classifying phrases into coherent clusters, predicated on the repetitions and similarities exposed through these neural network-driven techniques. This exploration was conducted using an extensive repository of folk songs, a treasure trove of foundational musical expressions that indelibly shape the stylistic contours of musical compositions. We claim that the presented approach not only opens avenues for penetrating and nuanced analysis, but also enriches our comprehension of the intricate interplay of musical components and their manifestations inspired by neural networks.