MusicDET models the distribution of real music features with frequency-guided normalizing flows to detect AI-generated music as out-of-distribution samples in a zero-shot setting.
From audio deepfake detection to AI-generated music detection — a pathway and overview
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ArtifactNet extracts codec residuals from spectrograms with a 4M-parameter network to detect AI music at F1=0.9829 and 1.49% FPR on unseen tracks from 22 generators, outperforming larger baselines.
The authors provide the first systematic benchmark of traditional ML, DNN, Transformer, state-space, and multimodal models for machine-generated music detection, augmented with XAI analysis, and report ResNet18 as the strongest performer on in-domain and out-of-domain tests.
AT-ADD introduces standardized tracks and datasets for evaluating audio deepfake detectors on speech under real-world conditions and on diverse unknown audio types to promote generalization beyond speech-centric methods.
citing papers explorer
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MusicDET: Zero-Shot AI-Generated Music Detection
MusicDET models the distribution of real music features with frequency-guided normalizing flows to detect AI-generated music as out-of-distribution samples in a zero-shot setting.
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ArtifactNet: Detecting AI-Generated Music via Forensic Residual Physics
ArtifactNet extracts codec residuals from spectrograms with a 4M-parameter network to detect AI music at F1=0.9829 and 1.49% FPR on unseen tracks from 22 generators, outperforming larger baselines.
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Explainable Detection of Machine Generated Music and Early Systematic Evaluation
The authors provide the first systematic benchmark of traditional ML, DNN, Transformer, state-space, and multimodal models for machine-generated music detection, augmented with XAI analysis, and report ResNet18 as the strongest performer on in-domain and out-of-domain tests.
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AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan
AT-ADD introduces standardized tracks and datasets for evaluating audio deepfake detectors on speech under real-world conditions and on diverse unknown audio types to promote generalization beyond speech-centric methods.