AI-Supported Analysis of Formal and Stylistic Transformations in Ottoman-Turkish Music (16th–19th Centuries)

Abstract

This study investigates the formal and stylistic transformations in Ottoman-Turkish music between the 16th and 19th centuries through artificial intelligence (AI)-assisted methodologies. While traditional musicology has often approached Ottoman music through qualitative and performance-centered analyses, this research offers a data-driven and computationally grounded alternative that leverages machine learning and digital humanities techniques to trace musical evolution across centuries. The corpus consists of 45 digitized compositions, including peşrevs and saz semais by three canonical composers, Buhurizâde Mustafa Itrî, Hammamizade İsmail Dede Efendi, and Hacı Arif Bey, representing different stylistic periods. These works were digitized, encoded in MusicXML format, and analyzed using symbolic music processing tools and unsupervised learning algorithms such as k-means clustering and hierarchical agglomerative clustering. Dimensionality reduction techniques like PCA and t-SNE enabled the visualization of stylistic proximities and divergences across periods. The study also addresses the epistemological challenges of applying AI to a non-Western, orally transmitted musical tradition. This research ultimately offers an empirical framework for analyzing stylistic transformation in Ottoman-Turkish music and opens new methodological pathways for studying modal, improvisational, and orally grounded musical traditions.