AI-driven Metadata Extraction and Semantic Search for Audiovisual Archives

ArchiveVault is a next-generation digital archiving system designed to enhance access to audiovisual collections through automated metadata extraction and advanced retrieval mechanisms. Traditional archiving methods are labor-intensive, requiring extensive manual annotation that often leads to incomplete and inconsistent metadata. ArchiveVault addresses this challenge by employing AI-based transcription, named entity recognition (NER), speaker diarization, and pose detection to extract structured metadata from audiovisual archives. This allows for rich, searchable metadata that improves retrieval precision beyond traditional keyword-based approaches. By leveraging state-of-the-art AI techniques, ArchiveVault enables researchers, archivists, and content creators to perform semantic searches across large collections, discovering moments of interest more effectively. Our deployments in a national broadcast archive (RTS) and the Olympic Games media collection demonstrate how AI-driven processing unlocks previously inaccessible content, from spoken-word analysis to pose-based retrieval for sports footage.