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.
Fakemusic- caps: a dataset for detection and attribution of synthetic music generated via text-to-music models,
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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.
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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.