用于多仪器声源分离的学习复杂掩模
Learned complex masks for multi-instrument source separation
摘要：
时频域中的音乐声源分离通常通过将软或二进制掩模应用于（复杂）频谱图的幅度分量来实现。相位分量通常不被估计，而是从混合物中复制并应用于估计的孤立源的幅度。虽然这种方法有几个实际的优点，它强加了一个上限的系统的性能，其中估计的孤立源固有地表现出可听的“相位伪影”。在本文中，我们解决这些缺点，直接估计掩码在复杂的域，扩展最近的工作从语音增强文献。该方法特别适合于多乐器音乐声源分离，因为残余相位伪像对于频谱重叠的乐器源（音乐中的常见情况）更明显。我们表明，复杂的面具比单独操作的幅度分量更好的分离。
英文摘要：
Music source separation in the time-frequency domain is commonly achieved by applying a soft or binary mask to the magnitude component of (complex) spectrograms. The phase component is usually not estimated, but instead copied from the mixture and applied to the magnitudes of the estimated isolated sources. While this method has several practical advantages, it imposes an upper bound on the performance of the system, where the estimated isolated sources inherently exhibit audible "phase artifacts". In this paper we address these shortcomings by directly estimating masks in the complex domain, extending recent work from the speech enhancement literature. The method is particularly well suited for multi-instrument musical source separation since residual phase artifacts are more pronounced for spectrally overlapping instrument sources, a common scenario in music. We show that complex masks result in better separation than masks that operate solely on the magnitude component.
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