Abstract
Music source restoration (MSR) aims to recover unprocessed stems from mixed and mastered recordings. The challenge lies in both separating overlapping sources and reconstructing signals degraded by production effects such as compression and reverberation. We therefore propose DTT-BSR, a hybrid generative adversarial network (GAN) combining rotary positional embeddings (RoPE) transformer for long-term temporal modeling with dual-path band-split recurrent neural network (RNN) for multi-resolution spectral processing. Our model achieved 3rd place on the objective leaderboard and 4th place on the subjective leaderboard on the ICASSP 2026 MSR Challenge, demonstrating exceptional generation fidelity and semantic alignment with a compact size of 7.1M parameters.
Abstract (translated)
音乐源恢复（MSR）的目标是从混合和母带处理的录音中恢复未经处理的音轨。挑战在于既要分离重叠的声音来源，又要重建因如压缩、混响等生产效果而退化的信号。因此，我们提出了DTT-BSR模型，这是一种结合了旋转位置嵌入（RoPE）变压器进行长期时序建模和双路径频带分割递归神经网络（RNN）进行多分辨率谱处理的混合生成对抗网络（GAN）。我们的模型在ICASSP 2026 MSR挑战赛中以7.1M参数量获得了客观排行榜第3名和主观排行榜第4名的成绩，展示了卓越的生成保真度和语义对齐能力。
URL
https://arxiv.org/abs/2602.19825
