Abstract
Music source separation is the task of isolating the instrumental tracks from a music song. Despite its spectacular recent progress, the trend towards more complex architectures and training protocols exacerbates reproducibility issues. The band-split recurrent neural networks (BSRNN) model is promising in this regard, since it yields close to state-of-the-art results on public datasets, and requires reasonable resources for training. Unfortunately, it is not straightforward to reproduce since its full code is not available. In this paper, we attempt to replicate BSRNN as closely as possible to the original paper through extensive experiments, which allows us to conduct a critical reflection on this reproducibility issue. Our contributions are three-fold. First, this study yields several insights on the model design and training pipeline, which sheds light on potential future improvements. In particular, since we were unsuccessful in reproducing the original results, we explore additional variants that ultimately yield an optimized BSRNN model, whose performance largely improves that of the original. Second, we discuss reproducibility issues from both methodological and practical perspectives. We notably underline how substantial time and energy costs could have been saved upon availability of the full pipeline. Third, our code and pre-trained models are released publicly to foster reproducible research. We hope that this study will contribute to spread awareness on the importance of reproducible research in the music separation community, and help promoting more transparent and sustainable practices.
Abstract (translated)
音乐源分离的任务是从一首歌曲中单独提取出乐器轨道。尽管这一领域近期取得了显著进展，但复杂架构和训练协议的发展加剧了再现性问题。带分频循环神经网络（BSRNN）模型在这一方面很有前景，因为它能在公开数据集上取得接近当前最佳的成果，并且需要合理的资源进行训练。然而，由于其完整代码不可用，它并不容易被复制。在这篇文章中，我们通过大量实验试图尽可能忠实地重现原始论文中的BSRNN，这使我们能够对这一再现性问题进行批判性的反思。 我们的贡献有三个方面： 1. 本研究提供了关于模型设计和训练流程的多个见解，为潜在未来改进提供了思路。特别是由于我们在复制原始结果方面不成功，我们探索了额外变体，并最终得到了一个优化后的BSRNN模型，其性能远超原版。 2. 我们从方法论和实践两个角度讨论了再现性问题。特别地，我们指出了如果整个流程完整可得，将能大幅节省时间和精力成本。 3. 我们的代码及预训练模型已公开发布，以促进可重复研究。希望这项研究有助于在音乐分离社区传播关于可重复研究重要性的认识，并帮助推广更加透明和可持续的做法。 通过上述努力，我们希望能对推进音乐源分离领域的科学进步做出贡献，同时也强调了开放性和透明性对于科学研究的重要性。
URL
https://arxiv.org/abs/2603.09187
