Evaluating Fake Music Detection Performance Under Audio Augmentations
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909688989220864 |
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| author | Sroka, Tomasz Wężowicz, Tomasz Sidorczuk, Dominik Modrzejewski, Mateusz |
| author_facet | Sroka, Tomasz Wężowicz, Tomasz Sidorczuk, Dominik Modrzejewski, Mateusz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_10447 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluating Fake Music Detection Performance Under Audio Augmentations Sroka, Tomasz Wężowicz, Tomasz Sidorczuk, Dominik Modrzejewski, Mateusz Sound Artificial Intelligence Machine Learning Audio and Speech Processing 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. |
| title | Evaluating Fake Music Detection Performance Under Audio Augmentations |
| topic | Sound Artificial Intelligence Machine Learning Audio and Speech Processing |
| url | https://arxiv.org/abs/2507.10447 |