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 $\sim$500 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.

翻译：查询式通用声音分离是智能听觉系统的基础，旨在从混合音频中分离特定声源。尽管近期取得进展，现有方法在复杂声学场景中仍存在残留干扰问题。这一性能局限主要源于数据瓶颈：真实场景数据集包含弱标签及严重的事件共现现象。这些缺陷导致模型学习背景噪声与目标类别间的伪相关性，而非鲁棒的声学特征。为解决此问题，我们提出一种自动化流程，通过语义一致性合成协议从真实数据集中挖掘高纯度单事件片段，从而消除事件共现。利用该流程，我们构建了Hive——一个包含2400小时原始音频的高质量合成数据集。实验结果表明，与在规模约500倍于Hive的庞大数据集上训练的最先进模型SAM-Audio相比，基于Hive训练的部分开源模型获得了具有竞争力的分离精度与感知质量。此外，这些模型在分布外评估基准上展现出卓越的零样本泛化能力。这些发现表明，优先保证监督信号的纯度可显著提升数据效率，为以更低计算成本训练鲁棒的听觉基础模型提供了新范式。代码与数据集公开于https://shandaai.github.io/Hive。