Distinguishing AI-Generated Music from Edited Audio as a Hard-Negative Robustness Task

[2608.14916] Distinguishing AI-Generated Music from Edited Audio as a Hard-Negative Robustness Task Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Sound arXiv:2608.14916 (cs) [Submitted on 14 Aug 2026] Title: Distinguishing AI-Generated Music from Edited Audio as a Hard-Negative Robustness Task Authors: Alexandru-Stefan Morosanu , Valerian Cecan , Stefan-Daniel Achirei , Laura Erhan View a PDF of the paper titled Distinguishing AI-Generated Music from Edited Audio as a Hard-Negative Robustness Task, by Alexandru-Stefan Morosanu and 3 other authors View PDF Abstract: AI-generated music detectors are commonly evaluated against original songs, but real-world uploads are often remixed, re-encoded, pitch-shifted, or otherwise edited. These edited versions form a difficult negative class: they are not generated by AI, yet they may introduce spectral artifacts that resemble synthetic audio fingerprints. We study this problem as a hard-negative robustness setting for AI-generated music detection, focusing on AI-generated and edited variants derived from the same anchor songs. We compile a YouTube-based dataset of AI, edited, and original variants, using the original tracks only as references, and train a binary AI versus edited detector. Audio is processed as 10-second clips and passed as raw waveforms to a pretrained PaSST spectrogram transformer. To reduce leakage, all splits are performed by anchor song. On the held-out test set, the final video-level system achieves 0.811 balanced accuracy. At clip level, AI-generated clips reach an F1-score of 0.836, while edited clips reach a lower F1-score of 0.720. The results suggest that AI-generated music retains detectable fingerprint-like spectral cues beyond ordinary editing, but the lower edited-class F1-score shows that these cues can still overlap with artifacts from edited audio. Grad-CAM visualizations are used to inspect whether high-confidence predictions rely on localized time-frequency regions. Comments: Accepted at the RobustifAI 2026 Workshop @IJCAI-ECAI 2026, Bremen Subjects: Sound (cs.SD) ; Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2608.14916 [cs.SD] (or arXiv:2608.14916v1 [cs.SD] for this version) https://doi.org/10.48550/arXiv.2608.14916 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Laura Erhan [ view email ] [v1] Fri, 14 Aug 2026 22:03:50 UTC (961 KB) Full-text links: Access Paper: View a PDF of the paper titled Distinguishing AI-Generated Music from Edited Audio as a Hard-Negative Robustness Task, by Alexandru-Stefan Morosanu and 3 other authors View PDF view license Current browse context: cs.SD < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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