论文标题
无监督的多通道源分离的空间损失
Spatial Loss for Unsupervised Multi-channel Source Separation
论文作者
论文摘要
我们提出了无监督的多通道源分离的空间损失。提议的损失利用了到达方向（DOA）和横梁形成的二元性：转向和横向成形向量应与目标源对齐，但要对其进行正交。空间损失分别促进了经典DOA估计器和神经分离器的混合系统之间的一致性。通过提出的损失，我们基于最小方差失真响应（MVDR）和独立矢量分析（IVA）训练神经分离器。我们还研究了将空间损失和信号损失相结合的有效性，该损失将盲源分离的输出作为参考。我们评估我们提出的关于合成和记录（图书馆）混合物的方法。我们发现，空间损失最有效地训练基于IVA的分离器。对于神经MVDR波束形式，与信号丢失结合使用时性能最好。在合成混合物上，提出的无监督损失导致与单词错误率有关的监督损失相同。在图书馆中，我们获得了无标记的培训数据的近乎最先进的性能。
We propose a spatial loss for unsupervised multi-channel source separation. The proposed loss exploits the duality of direction of arrival (DOA) and beamforming: the steering and beamforming vectors should be aligned for the target source, but orthogonal for interfering ones. The spatial loss encourages consistency between the mixing and demixing systems from a classic DOA estimator and a neural separator, respectively. With the proposed loss, we train the neural separators based on minimum variance distortionless response (MVDR) beamforming and independent vector analysis (IVA). We also investigate the effectiveness of combining our spatial loss and a signal loss, which uses the outputs of blind source separation as the reference. We evaluate our proposed method on synthetic and recorded (LibriCSS) mixtures. We find that the spatial loss is most effective to train IVA-based separators. For the neural MVDR beamformer, it performs best when combined with a signal loss. On synthetic mixtures, the proposed unsupervised loss leads to the same performance as a supervised loss in terms of word error rate. On LibriCSS, we obtain close to state-of-the-art performance without any labeled training data.
