# Multitask learning for instrument activation aware music source separation

arXiv: https://arxiv.org/abs/2008.00616 · HTML: https://mlsanity.com/paper/2008.00616/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Aug 2020.

## Built on by (most cited first, 3 of 3)

- [A Unified Model for Zero-shot Music Source Separation, Transcription and Synthesis](https://mlsanity.com/paper/2108.03456/) (2021)
- [Multi-task Learning with Metadata for Music Mood Classification](https://mlsanity.com/paper/2110.04765/) (2021)
- [Activity-Guided Industrial Anomalous Sound Detection against Interferences](https://mlsanity.com/paper/2409.01885/) (2024)

## Abstract (arXiv)

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.