用合成数据训练模型，分离巴西桑巴鼓的音源。
Musical Source Separation of Brazilian Percussion
- 基于重复节奏和低频音色特征，用U-Net模型分离桑巴鼓。
- 仅用有限数据即实现有效分离，准确率表现良好。
- 为非西方音乐源分离提供低成本可行方案，适合文化多样性研究者。
音乐源分离（MSS）在西方音乐乐器分离方面取得显著进展，但针对非西方乐器的研究仍受限于数据不足。本演示利用现有的巴西萨马打击乐数据集，生成人工混合音轨，训练U-Net模型以分离桑巴音乐中的苏尔多鼓（surdo）。尽管训练数据有限，由于该鼓具有重复性节奏模式和典型低频音色特征，模型仍能有效分离出目标音源。结果表明，无需大规模数据采集，MSS系统亦可应用于更包容多元文化的场景，推动跨文化音乐处理的发展。
原文摘要 · Abstract (English)
Musical source separation (MSS) has recently seen a big breakthrough in separating instruments from a mixture in the context of Western music, but research on non-Western instruments is still limited due to a lack of data. In this demo, we use an existing dataset of Brazilian sama percussion to create artificial mixtures for training a U-Net model to separate the surdo drum, a traditional instrument in samba. Despite limited training data, the model effectively isolates the surdo, given the drum's repetitive patterns and its characteristic low-pitched timbre. These results suggest that MSS systems can be successfully harnessed to work in more culturally-inclusive scenarios without the need of collecting extensive amounts of data.
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