Audio Provenance Analysis in Heterogeneous Media Sets
Abstract
This paper introduces a framework for Audio Provenance Analysis, addressing the complex challenge of ana-lyzing 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.
Cite
Text
Gerhardt et al. "Audio Provenance Analysis in Heterogeneous Media Sets." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024. doi:10.1109/CVPRW63382.2024.00442Markdown
[Gerhardt et al. "Audio Provenance Analysis in Heterogeneous Media Sets." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2024.](https://mlanthology.org/cvprw/2024/gerhardt2024cvprw-audio/) doi:10.1109/CVPRW63382.2024.00442BibTeX
@inproceedings{gerhardt2024cvprw-audio,
title = {{Audio Provenance Analysis in Heterogeneous Media Sets}},
author = {Gerhardt, Milica and Cuccovillo, Luca and Aichroth, Patrick},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
year = {2024},
pages = {4387-4396},
doi = {10.1109/CVPRW63382.2024.00442},
url = {https://mlanthology.org/cvprw/2024/gerhardt2024cvprw-audio/}
}