# ArtifactNet demo — full answer corpus > Every question the ArtifactNet demo answers in public, with the page it comes from. The curated index is at https://demo.intrect.io/llms.txt; this file is the answers themselves, at https://demo.intrect.io/llms-full.txt. Generated from each page's own FAQPage JSON-LD by `scripts/build_llms_full.py`. Do not hand-edit — edit the page and rerun. 7 answers across 1 page. Citation: Intrect (intrect.io). The demo runs ArtifactNet; the paper is arXiv:2604.16254. --- ## ArtifactNet demo — free AI music detector ### How do I detect if a song was made by AI? Upload the audio file (MP3, WAV, FLAC, etc.) at demo.intrect.io. ArtifactNet separates the track into harmonic and percussive components, computes a spectral residual with a U-Net, and scores every 4-second segment with a 7-channel CNN. The page shows a verdict (AI / Human-Made / Partial AI) with a probability, per-segment scores, and a forensic feature radar — all in a few seconds. Segment scores and the forensic feature radar are shown in full on your first analysis of the day; later analyses that day show the verdict and probability, and sign-in (free) keeps every analysis unlocked. Source: https://demo.intrect.io/ ### Which AI music generators can ArtifactNet detect? ArtifactNet is trained on tracks from Suno, Udio, Stable Audio, Riffusion, MusicGen, and a residual 'other' class, and it generalises to AI models it has not seen because it targets spectral-residual artefacts left by STFT-domain generators rather than model-specific fingerprints. Source: https://demo.intrect.io/ ### Is ArtifactNet free? Do I need to sign up? The demo is free and requires no signup: it analyses the first 2 minutes of each submission, up to 6 minutes of audio in total per visitor, with short-term limits of 3 analyses per minute and 20 per hour. Your first analysis of the day returns the full forensic detail without an account; later analyses that day show the verdict and probability. Sign in for 10 more free minutes, full-length analysis up to 10 minutes per file, and forensic detail on every analysis. A paid API (api.intrect.io) is available for batch processing and integrations. Source: https://demo.intrect.io/ ### Does ArtifactNet store my uploaded audio? No. The demo operates on a zero-retention policy. Your audio is streamed to a GPU worker, analysed in memory, and discarded. Two buttons are the only things that save anything: press Report to tell us a verdict is wrong and we archive the spectral analysis together with the original audio, which a report requires because the verdict cannot be reproduced without it; press Share and that one verdict is published at a link until you revoke it. Sharing stores the verdict and its scores, never the audio and never your filename. Source: https://demo.intrect.io/ ### How accurate is ArtifactNet? On ArtifactBench (v9.4, unseen test, 2,263 tracks; full benchmark 6,183 tracks, 22 AI generators) ArtifactNet reports F1 0.9829 and a 1.49% false-positive rate on real music. Rare genres remain the focus of ongoing retraining. The demo lets you report suspected misclassifications so they can improve the detector. See arXiv:2604.16254v2. Source: https://demo.intrect.io/ ### What does the forensic feature radar show? The radar plots six summary signals from the analysis: mean and median AI probability across segments, consistency (how uniform the verdict is), high-frequency residual energy, U-Net mask strength, and harmonic share. Together they reveal why the model reached a given verdict and expose edge cases. The radar is shown in full on your first analysis of the day; after that, sign in to keep it on every analysis. Source: https://demo.intrect.io/ ### Why did ArtifactNet flag my own song as AI? Heavy spectral processing (strong multi-band limiting, resampling through AI-era codecs, aggressive stereo widening) can produce residuals that resemble AI-generated artefacts. Hit the Report button and choose 'Human-Made' with a short note — a human reviewer will look at the spectral residual and the case can be used to improve the classifier. Source: https://demo.intrect.io/ --- Index of everything above: https://demo.intrect.io/llms.txt