Evaluating Fake Music Detection Performance Under Audio Augmentations

• URL: http://arxiv.org/abs/2507.10447v1
• Date: Mon, 07 Jul 2025 16:15:02 GMT
• Title: Evaluating Fake Music Detection Performance Under Audio Augmentations
• Authors: Tomasz Sroka, Tomasz Wężowicz, Dominik Sidorczuk, Mateusz Modrzejewski,
• Abstract summary: We construct a dataset consisting of both real and synthetic music generated using several systems.<n>We then apply a range of audio transformations and analyze how they affect classification accuracy.<n>We test the performance of a recent state-of-the-art musical deepfake detection model in the presence of audio augmentations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the rapid advancement of generative audio models, distinguishing between human-composed and generated music is becoming increasingly challenging. As a response, models for detecting fake music have been proposed. In this work, we explore the robustness of such systems under audio augmentations. To evaluate model generalization, we constructed a dataset consisting of both real and synthetic music generated using several systems. We then apply a range of audio transformations and analyze how they affect classification accuracy. We test the performance of a recent state-of-the-art musical deepfake detection model in the presence of audio augmentations. The performance of the model decreases significantly even with the introduction of light augmentations.

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