Archaeological Insights into AI-Generated Music and Copyright Protection in the Mediterranean Region

Abstract

This paper explores the ancient musical heritage discovered in archaeological sites in the Mediterranean region and addresses the contemporary issue of copyright protection for AI-generated music. Through an interdisciplinary approach combining archaeology, archaeometry, and digital archaeology, we propose an innovative algorithm-assisted framework. We develop a Pinecone Optimization-driven batch redefined recurrent neural network (PCO-BRRNN) for detecting AI-generated music content and evaluating its authenticity. During the study, we compiled a dataset containing various music files, including AI-generated works and real recordings, along with labels indicating the authenticity status of each piece. The algorithm was trained and evaluated to promote anti-piracy and authentication goals. By iteratively adjusting network parameters, PCO-BRRNN optimizes model performance to accurately detect AI-generated music and verify its authenticity. This research not only provides new methods for the preservation of ancient musical heritage but also offers robust tools for copyright protection in the modern digital music market.