SingVERSE: A Diverse, Real-World Benchmark for Singing Voice Enhancement
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | |
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| _version_ | 1866912604661743616 |
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| author | Jiang, Shaohan Zhang, Junan Zhang, Yunjia Yang, Jing Fan, Fan Wu, Zhizheng |
| author_facet | Jiang, Shaohan Zhang, Junan Zhang, Yunjia Yang, Jing Fan, Fan Wu, Zhizheng |
| contents | This paper presents a benchmark for singing voice enhancement. The development of singing voice enhancement is limited by the lack of realistic evaluation data. To address this gap, this paper introduces SingVERSE, the first real-world benchmark for singing voice enhancement, covering diverse acoustic scenarios and providing paired, studio-quality clean references. Leveraging SingVERSE, we conduct a comprehensive evaluation of state-of-the-art models and uncover a consistent trade-off between perceptual quality and intelligibility. Finally, we show that training on in-domain singing data substantially improves enhancement performance without degrading speech capabilities, establishing a simple yet effective path forward. This work offers the community a foundational benchmark together with critical insights to guide future advances in this underexplored domain. Demopage: https://singverse.github.io |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_20969 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SingVERSE: A Diverse, Real-World Benchmark for Singing Voice Enhancement Jiang, Shaohan Zhang, Junan Zhang, Yunjia Yang, Jing Fan, Fan Wu, Zhizheng Sound Audio and Speech Processing This paper presents a benchmark for singing voice enhancement. The development of singing voice enhancement is limited by the lack of realistic evaluation data. To address this gap, this paper introduces SingVERSE, the first real-world benchmark for singing voice enhancement, covering diverse acoustic scenarios and providing paired, studio-quality clean references. Leveraging SingVERSE, we conduct a comprehensive evaluation of state-of-the-art models and uncover a consistent trade-off between perceptual quality and intelligibility. Finally, we show that training on in-domain singing data substantially improves enhancement performance without degrading speech capabilities, establishing a simple yet effective path forward. This work offers the community a foundational benchmark together with critical insights to guide future advances in this underexplored domain. Demopage: https://singverse.github.io |
| title | SingVERSE: A Diverse, Real-World Benchmark for Singing Voice Enhancement |
| topic | Sound Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.20969 |