# MSRBench: A Benchmarking Dataset for Music Source Restoration

arXiv: https://arxiv.org/abs/2510.10995 · HTML: https://mlsanity.com/paper/2510.10995/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2025.

## Built on by (most cited first, 4 of 4)

- [Summary of The Inaugural Music Source Restoration Challenge](https://mlsanity.com/paper/2601.04343/) (2026)
- [Multi-Stage Music Source Restoration with BandSplit-RoFormer Separation and HiFi++ GAN](https://mlsanity.com/paper/2603.04032/) (2026)
- [Music Source Restoration with Ensemble Separation and Targeted Reconstruction](https://mlsanity.com/paper/2603.16926/) (2026)
- [DTT-BSR+: A Generative-Regression Cascade for Music Source Restoration](https://mlsanity.com/paper/2606.24127/) (2026)

## Abstract (arXiv)

Music Source Restoration (MSR) extends source separation to realistic settings where signals undergo production effects (equalization, compression, reverb) and real-world degradations, with the goal of recovering the original unprocessed sources. Existing benchmarks cannot measure restoration fidelity: synthetic datasets use unprocessed stems but unrealistic mixtures, while real production datasets provide only already-processed stems without clean references. We present MSRBench, the first benchmark explicitly designed for MSR evaluation. MSRBench contains raw stem-mixture pairs across eight instrument classes, where mixtures are produced by professional mixing engineers. These raw-processed pairs enable direct evaluation of both separation accuracy and restoration fidelity. Beyond controlled studio conditions, the mixtures are augmented with twelve real-world degradations spanning analog artifacts, acoustic environments, and lossy codecs. Baseline experiments with U-Net and BSRNN achieve SI-SNR of -37.8 dB and -23.4 dB respectively, with perceptual quality (FAD CLAP) around 0.7-0.8, demonstrating substantial room for improvement and the need for restoration-specific architectures.