# Summary of The Inaugural Music Source Restoration Challenge

arXiv: https://arxiv.org/abs/2601.04343 · HTML: https://mlsanity.com/paper/2601.04343/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 7 Jan 2026.

## Builds on (most cited first, 17 of 17)

- [Large-scale Contrastive Language-Audio Pretraining with Feature Fusion and Keyword-to-Caption Augmentation](https://mlsanity.com/paper/2211.06687/) (2022)
- [High Fidelity Neural Audio Compression](https://mlsanity.com/paper/2210.13438/) (2022)
- [High-Fidelity Audio Compression with Improved RVQGAN](https://mlsanity.com/paper/2306.06546/) (2023)
- [Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset](https://mlsanity.com/paper/1810.12247/) (2018)
- [Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation](https://mlsanity.com/paper/1806.03185/) (2018)
- [Music Source Separation with Band-split RNN](https://mlsanity.com/paper/2209.15174/) (2022)
- [Creating A Multi-track Classical Musical Performance Dataset for Multimodal Music Analysis: Challenges, Insights, and Applications](https://mlsanity.com/paper/1612.08727/) (2016)
- [Moisesdb: A dataset for source separation beyond 4-stems](https://mlsanity.com/paper/2307.15913/) (2023)
- [Music Demixing Challenge 2021](https://mlsanity.com/paper/2108.13559/) (2021)
- [Music Source Separation with Band-Split RoPE Transformer](https://mlsanity.com/paper/2309.02612/) (2023)
- [Mel-Band RoFormer for Music Source Separation](https://mlsanity.com/paper/2310.01809/) (2023)
- [The Sound Demixing Challenge 2023 unicodex2013 Cinematic Demixing Track](https://mlsanity.com/paper/2308.06981/) (2023)
- [Music Source Separation Based on a Lightweight Deep Learning Framework (DTTNET: DUAL-PATH TFC-TDF UNET)](https://mlsanity.com/paper/2309.08684/) (2023)
- [Benchmarks and leaderboards for sound demixing tasks](https://mlsanity.com/paper/2305.07489/) (2023)
- [Zimtohrli: An Efficient Psychoacoustic Audio Similarity Metric](https://mlsanity.com/paper/2509.26133/) (2025)
- [Music Source Restoration](https://mlsanity.com/paper/2505.21827/) (2025)
- [MSRBench: A Benchmarking Dataset for Music Source Restoration](https://mlsanity.com/paper/2510.10995/) (2025)

## Built on by (most cited first, 1 of 1)

- [DTT-BSR+: A Generative-Regression Cascade for Music Source Restoration](https://mlsanity.com/paper/2606.24127/) (2026)

## Abstract (arXiv)

Music Source Restoration (MSR) aims to recover original, unprocessed instrument stems from professionally mixed and degraded audio, requiring the reversal of both production effects and real-world degradations. We present the inaugural MSR Challenge, which features objective evaluation on studio-produced mixtures using Multi-Mel-SNR, Zimtohrli, and FAD-CLAP, alongside subjective evaluation on real-world degraded recordings. Five teams participated in the challenge. The winning system achieved 4.46 dB Multi-Mel-SNR and 3.47 MOS-Overall, corresponding to relative improvements of 91% and 18% over the second-place system, respectively. Per-stem analysis reveals substantial variation in restoration difficulty across instruments, with bass averaging 4.59 dB across all teams, while percussion averages only 0.29 dB. The dataset, evaluation protocols, and baselines are available at https://msrchallenge.com/.