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Recent years have witnessed the success of deep learning on the visual soundseparation task. However, existing works follow similar settings where thetraining and testing datasets share the same musical instrument categories,which to some extent limits the versatility of this task. In this work, wefocus on a more general and challenging scenario, namely the separation ofunknown musical instruments, where the categories in training and testingphases have no overlap with each other. To tackle this new setting, we proposethe Separation-with-Consistency (SeCo) framework, which can accomplish theseparation on unknown categories by exploiting the consistency constraints.Furthermore, to capture richer characteristics of the novel melodies, we devisean online matching strategy, which can bring stable enhancements with no costof extra parameters. Experiments demonstrate that our SeCo framework exhibitsstrong adaptation ability on the novel musical categories and outperforms thebaseline methods by a significant margin.

SeCo: Separating Unknown Musical Visual Sounds with Consistency Guidance