标题： 一种语义一致的数据集用于数据高效的基于查询的通用声音分离 显示英文标题

标题： A Semantically Consistent Dataset for Data-Efficient Query-Based Universal Sound Separation

摘要： 基于查询的通用声音分离是智能听觉系统的基础，旨在从混合信号中隔离特定声源。 尽管最近取得了进展，现有方法在复杂的声学场景中仍持续受到残留干扰的影响。 这种性能限制主要源于数据瓶颈：真实环境数据集包含弱标签和严重的事件共现。 这些缺陷导致模型学习背景噪声与目标类别之间的虚假相关性，而不是稳健的声学特征。 为了解决这个问题，我们提出了一种自动化流程，通过语义一致的合成协议从真实环境数据集中挖掘高纯度单事件片段，从而消除事件的共现。 利用该流程，我们构建了Hive，一个高质量的合成数据集，包含2.4千小时的原始音频。 实验结果表明，与在比Hive大500倍的数据集$\sim$上训练的最先进的SAM-Audio模型相比，某些在Hive上训练的开源模型实现了具有竞争力的分离准确性和感知质量。 此外，这些模型在分布外评估基准上表现出显著的零样本泛化能力。 这些发现表明，优先考虑监督信号的纯净度可以实现显著的数据效率，为以减少计算成本的方式训练稳健的听觉基础模型提供了新范式。 代码和数据集可在https://shandaai.github.io/Hive获取。 显示英文摘要

摘要： 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.