Multi-Stage Music Source Restoration with BandSplit-RoFormer Separation and HiFi++ GAN

TL;DR

This technical report presents the CP-JKU team's system for the MSR ICASSP Challenge 2025, which applies a HiFi++ GAN waveform restorer trained as a generalist and then specialized into eight instrument-specific experts.

Abstract

Music Source Restoration (MSR) targets recovery of original, unprocessed instrument stems from fully mixed and mastered audio, where production effects and distribution artifacts violate common linear-mixture assumptions. This technical report presents the CP-JKU team's system for the MSR ICASSP Challenge 2025. Our approach decomposes MSR into separation and restoration. First, a single BandSplit-RoFormer separator predicts eight stems plus an auxiliary other stem, and is trained with a three-stage curriculum that progresses from 4-stem warm-start fine-tuning (with LoRA) to 8-stem extension via head expansion. Second, we apply a HiFi++ GAN waveform restorer trained as a generalist and then specialized into eight instrument-specific experts.