SpaIn-Net：空间信息立体声音乐源分离
SpaIn-Net: Spatially-Informed Stereophonic Music Source Separation
摘要：
随着最近使用深度神经网络的数据驱动方法的进步，音乐源分离已被制定为特定于乐器的监督问题。虽然现有的深度学习模型隐含地吸收了多通道输入信号所传达的空间信息，但我们认为，更明确和积极地使用空间信息不仅可以改善分离过程，还可以为许多基于用户交互的工具提供入口点。为此，我们介绍了一种控制方法的基础上的立体声感兴趣的源的位置，表示为摇摄角。我们提出了各种条件反射机制，包括使用原始角度及其衍生的功能表示，并表明空间信息的帮助。我们提出的方法提高了分离性能相比，位置不可知的架构由1.8 dB SI-SDR在我们的Slakh为基础的模拟实验。此外，所提出的方法允许解开同类乐器，例如，在包含两个吉他音轨的混合物。最后，我们还证明了我们的方法是强大的不正确的源平移信息，这可能会导致我们提出的用户交互。
英文摘要：
With the recent advancements of data driven approaches using deep neural networks, music source separation has been formulated as an instrument-specific supervised problem. While existing deep learning models implicitly absorb the spatial information conveyed by the multi-channel input signals, we argue that a more explicit and active use of spatial information could not only improve the separation process but also provide an entry-point for many user-interaction based tools. To this end, we introduce a control method based on the stereophonic location of the sources of interest, expressed as the panning angle. We present various conditioning mechanisms, including the use of raw angle and its derived feature representations, and show that spatial information helps. Our proposed approaches improve the separation performance compared to location agnostic architectures by 1.8 dB SI-SDR in our Slakh-based simulated experiments. Furthermore, the proposed methods allow for the disentanglement of same-class instruments, for example, in mixtures containing two guitar tracks. Finally, we also demonstrate that our approach is robust to incorrect source panning information, which can be incurred by our proposed user interaction.
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