论文标题
仪器激活的多任务学习音乐源分离
Multitask learning for instrument activation aware music source separation
论文作者
论文摘要
音乐源分离是音乐信息检索的核心任务，在过去的几年中，它取得了巨大的改善。然而，大多数现有系统仅专注于源分离本身的问题，而忽略了其他〜--可能相关的 - 可能会导致更多质量收益的MIR任务。在这项工作中，我们提出了一种新型的多任务结构，以使用仪器激活信息来调查以提高源分离性能。此外，我们通过利用MedleyDB的组合和混合秘密数据集的组合来研究与六个独立仪器有关的系统，这是一种比广泛使用的MUSDB数据集中包含的三种仪器更现实的场景。结果表明，我们提出的多任务模型的表现优于混合秘密和MedleyDB数据集的混合物基线开放式模型，同时保持MUSDB数据集的可比性。
Music source separation is a core task in music information retrieval which has seen a dramatic improvement in the past years. Nevertheless, most of the existing systems focus exclusively on the problem of source separation itself and ignore the utilization of other~---possibly related---~MIR tasks which could lead to additional quality gains. In this work, we propose a novel multitask structure to investigate using instrument activation information to improve source separation performance. Furthermore, we investigate our system on six independent instruments, a more realistic scenario than the three instruments included in the widely-used MUSDB dataset, by leveraging a combination of the MedleyDB and Mixing Secrets datasets. The results show that our proposed multitask model outperforms the baseline Open-Unmix model on the mixture of Mixing Secrets and MedleyDB dataset while maintaining comparable performance on the MUSDB dataset.
