🤖 AI Summary
This work addresses the challenging task of recovering original, unprocessed stems from fully mixed and mastered music recordings. The proposed approach employs a two-stage pipeline: first, it aggregates outputs from multiple pre-trained source separation models to obtain initial stem estimates; second, it applies a dedicated BSRNN-based restoration model to each estimated stem to reverse complex audio degradations such as equalization, dynamic range compression, and reverberation. By uniquely combining ensemble-based separation with targeted restoration, the method effectively mitigates the compounded distortions present in real-world audio. Evaluated on the official MSR benchmark, the proposed system achieves the second-highest overall score, demonstrating substantial improvement over existing baselines.
📝 Abstract
The Inaugural Music Source Restoration (MSR) Challenge targets the recovery of original, unprocessed stems from fully mixed and mastered music. Unlike conventional music source separation, MSR requires reversing complex production processes such as equalization, compression, reverberation, and other real-world degradations. To address MSR, we propose a two-stage system. First, an ensemble of pre-trained separation models produces preliminary source estimates. Then a set of pre-trained BSRNN-based restoration models performs targeted reconstruction to refine these estimates. On the official MSR benchmark, our system surpasses the baselines on all metrics, ranking second among all submissions. The code is available at https://github.com/xinghour/Music-source-restoration-CUPAudioGroup