A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation
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. These flaws induce models to learn spurious correlations between background noise and target categories instead of robust acoustic features. To address this, we propose an automated pipeline that eliminates co-occurrence of events by mining high-purity single-event segments from in-the-wild datasets via a semantically consistent synthesis protocol. Utilizing this pipeline, we constructed Hive, a high-quality synthetic dataset comprising 2.4k hours of raw audio. Experimental results demonstrate that, compared with the state-of-the-art model SAM-Audio which was trained on a huge dataset sim500 times larger than Hive, certain open-source models trained on Hive achieve competitive separation accuracy and perceptual quality. Moreover, these models exhibited remarkable zero-shot generalization on out-of-distribution evaluation benchmarks. These findings highlight that prioritizing purity of supervised signals enables significant data efficiency, offering a new paradigm for training robust auditory foundation models with reduced computational costs. Code and dataset are available at https://shandaai.github.io/Hive.
698247f19084cb4f0ecb54be
Kai Li
698247f19084cb4f0ecb54bf
Jintao Cheng
698247f19084cb4f0ecb54c0
Chang Zeng
claimed_verified
2026-03-23T11:01:31.952Z
62597cb0c53775eff9eba55f
/avatars/1e8cd225cac80fb91583d1a59da58aea.svg
Chang Zeng
woshinibaba
user
woshinibaba
698247f19084cb4f0ecb54c1
Zijun Yan
698247f19084cb4f0ecb54c2
Helin Wang
698247f19084cb4f0ecb54c3
Zixiong Su
698247f19084cb4f0ecb54c4
Bo Zheng
698247f19084cb4f0ecb54c5
Xiaolin Hu
A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation
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. These flaws induce models to learn spurious correlations between background noise and target categories instead of robust acoustic features. To address this, we propose an automated pipeline that eliminates co-occurrence of events by mining high-purity single-event segments from in-the-wild datasets via a semantically consistent synthesis protocol. Utilizing this pipeline, we constructed Hive, a high-quality synthetic dataset comprising 2.4k hours of raw audio. Experimental results demonstrate that, compared with the state-of-the-art model SAM-Audio which was trained on a huge dataset sim500 times larger than Hive, certain open-source models trained on Hive achieve competitive separation accuracy and perceptual quality. Moreover, these models exhibited remarkable zero-shot generalization on out-of-distribution evaluation benchmarks. These findings highlight that prioritizing purity of supervised signals enables significant data efficiency, offering a new paradigm for training robust auditory foundation models with reduced computational costs. Code and dataset are available at https://shandaai.github.io/Hive.
Automated pipeline for sound separation using high-purity single-event segments from in-the-wild datasets achieves competitive performance with significantly reduced data requirements.
universal sound separation in-the-wild datasets co-occurrence of events semantically consistent synthesis protocol high-purity single-event segments Hive dataset SAM-Audio zero-shot generalization auditory foundation models data efficiency