Data-Efficient Query-Based Universal Sound Separation
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A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation
Announce Type: replace Abstract: Query-based universal sound separation is fundamental to intelligent auditory systems, aiming to isolate specific sources from mixtures. Despite recent advances, existing methods continue to suffer from residual interference in complex acoustic scenes. This performance limitation stems largely from a data bottleneck: in-the-wild datasets contain weak labels and severe co-occurrence of events.