Recent advances in target sound extraction (TSE) utilize directional clues derived from direction of arrival (DoA), which represent an inherent spatial property of sound available in any acoustic scene. However, previous DoA-based methods rely on hand-crafted features or discrete encodings, which lose fine-grained spatial information and limit adaptability. We propose SoundCompass, an effective directional clue integration framework centered on a Spectral Pairwise INteraction (SPIN) module that captures cross-channel spatial correlations in the complex spectrogram domain to preserve full spatial information in multichannel signals. The input feature expressed in terms of spatial correlations is fused with a DoA clue represented as spherical harmonics (SH) encoding. The fusion is carried out across overlapping frequency subbands, inheriting the benefits reported in the previous band-split architectures. We also incorporate the iterative refinement strategy, chain-of-inference (CoI), in the TSE framework, which recursively fuses DoA with sound event activation estimated from the previous inference stage. Experiments demonstrate that SoundCompass, combining SPIN, SH embedding, and CoI, robustly extracts target sources across diverse signal classes and spatial configurations.

翻译：目标声音提取领域的最新进展利用了源自到达方向的方向线索，该线索代表了任何声学场景中声音固有的空间属性。然而，先前基于DoA的方法依赖于手工特征或离散编码，这些方法会丢失细粒度的空间信息并限制适应性。我们提出了SoundCompass，一个有效的方向线索整合框架，其核心是一个谱域成对交互模块，该模块在复数谱图域中捕获跨通道的空间相关性，以保留多通道信号中的完整空间信息。以空间相关性形式表达的输入特征，与以球谐函数编码表示的DoA线索进行融合。该融合在重叠的频率子带上进行，继承了先前频带分割架构所报告的优势。我们还在TSE框架中引入了迭代优化策略——推理链，该策略递归地将DoA与从前一推理阶段估计出的声音事件激活进行融合。实验表明，结合了SPIN、SH嵌入和CoI的SoundCompass能够稳健地提取不同信号类别和空间配置下的目标声源。