{"ID":2850774,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.21659","arxiv_id":"2510.21659","title":"Smule Renaissance Small: Efficient General-Purpose Vocal Restoration","abstract":"Vocal recordings on consumer devices commonly suffer from multiple concurrent degradations: noise, reverberation, band-limiting, and clipping. We present Smule Renaissance Small (SRS), a compact single-stage model that performs end-to-end vocal restoration directly in the complex STFT domain. By incorporating phase-aware losses, SRS enables large analysis windows for improved frequency resolution while achieving 10.5x real-time inference on iPhone 12 CPU at 48 kHz. On the DNS 5 Challenge blind set, despite no speech training, SRS outperforms a strong GAN baseline and closely matches a computationally expensive flow-matching system. To enable evaluation under realistic multi-degradation scenarios, we introduce the Extreme Degradation Bench (EDB): 87 singing and speech recordings captured under severe acoustic conditions. On EDB, SRS surpasses all open-source baselines on singing and matches commercial systems, while remaining competitive on speech despite no speech-specific training. We release both SRS and EDB under the MIT License.","short_abstract":"Vocal recordings on consumer devices commonly suffer from multiple concurrent degradations: noise, reverberation, band-limiting, and clipping. We present Smule Renaissance Small (SRS), a compact single-stage model that performs end-to-end vocal restoration directly in the complex STFT domain. By incorporating phase-awa...","url_abs":"https://arxiv.org/abs/2510.21659","url_pdf":"https://arxiv.org/pdf/2510.21659v1","authors":"[\"Yongyi Zang\",\"Chris Manchester\",\"David Young\",\"Ivan Ivanov\",\"Jeffrey Lufkin\",\"Martin Vladimirov\",\"PJ Solomon\",\"Svetoslav Kepchelev\",\"Fei Yueh Chen\",\"Dongting Cai\",\"Teodor Naydenov\",\"Randal Leistikow\"]","published":"2025-10-24T17:14:12Z","proceeding":"cs.SD","tasks":"[\"cs.SD\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}