Audio Provenance Analysis in Heterogeneous Media Sets

Milica Gerhardt, Luca Cuccovillo, Patrick Aichroth; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 4387-4396

This paper introduces a framework for Audio Provenance Analysis addressing the complex challenge of analyzing heterogeneous sets of audio items without requiring any prior knowledge of their content. Our framework applies a novel approach that combines partial audio matching and phylogeny techniques. It constructs directed acyclic graphs to capture the origins and the evolution of content within near-duplicate audio clusters identifying the least altered versions and tracing the reuse of content within these clusters. The approach is evaluated for two selected application scenarios demonstrating that it can accurately determine the direction of content reuse and identify parent-child relationships while also offering a dedicated dataset for benchmarking future research in this area.