Multitask learning for instrument activation aware music source separation

Yun-Ning Hung; Alexander Lerch

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arXiv · 2008.00616

Multitask learning for instrument activation aware music source separation

Yun-Ning Hung·Alexander Lerch

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Abstract

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.

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Yun-Ning Hung, Alexander Lerch. 2020-08-03. Multitask learning for instrument activation aware music source separation. https://arxiv.org/abs/2008.00616

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