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October 3, 2025Open Access

Evaluating Fake Music Detection Performance Under Audio Augmentations

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This work explores how audio augmentations impact classification accuracy in detecting fake music, suggesting a decline in model performance.

The classification accuracy of deepfake detection models decreases significantly with audio augmentations.
Testing revealed that even light audio augmentations notably impacted the model's performance on detecting synthetic music.
A dataset was constructed of both real and synthetic music to better evaluate model generalization under different audio conditions.
The study emphasizes the challenges posed by generative audio models in distinguishing human-composed music from generated compositions.

Sroka et al. (2025) studied this question.

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October 3, 2025Open Access

View Full Paper

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Authors

This work explores how audio augmentations impact classification accuracy in detecting fake music, suggesting a decline in model performance.

The classification accuracy of deepfake detection models decreases significantly with audio augmentations.
Testing revealed that even light audio augmentations notably impacted the model's performance on detecting synthetic music.
A dataset was constructed of both real and synthetic music to better evaluate model generalization under different audio conditions.
The study emphasizes the challenges posed by generative audio models in distinguishing human-composed music from generated compositions.

Sroka et al. (2025) studied this question.

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