EXPLORING ANCIENT MUSIC RECONSTRUCTION USING ARTIFICIAL INTELLIGENCE

Abstract

The article explores existing developments in the field of reconstructing ancient music using artificial intelligence, providing an overview of the theoretical and practical aspects of the advancement of this technology. It analyzes the tools used for digital processing and the reproduction of the sound of lost musical instruments. Particular attention is given to the historical context of the evolution of artificial intelligence and neural networks, starting from the creation of fundamental concepts and mathematical models developed by medieval scholars to modern deep learning algorithms.The study also examines the evolution of computational hardware, from the earliest computing devices that emerged before our era to modern supercomputers and grid networks that enable the analysis of complex acoustic data. Special focus is placed on the use of artificial intelligence in the music industry in general and, more specifically, for reconstructing the sound of lost musical instruments from ancient civilizations, such as the epigonion, lyre, aulos, and others.Attention is also given to the interdisciplinary approach, which involves collaboration among musicologists, cultural scholars, archaeologists, physicists, sound engineers, mathematicians, software engineers, and others. The implementation of such projects requires significant computational resources, funding, and access to archival and archaeological materials. At the same time, this reconstruction has not only scientific and technological value but also significant cultural and educational importance, as it allows for the recreation of sounds lost through the ages, contributes to a deeper understanding of past musical cultures, and opens new horizons for musicology and the digital industry as a whole.