This paper relates to the separation of single channel source signals from a single mixed signal by means of independent component analysis (ICA). The proposed idea lies in a time-frequency representation of the mixed signal and the use of ICA on spectral rows corresponding to different time intervals. In our approach, in order to reconstruct true sources, we proposed a novelty idea of grouping statistically independent time-frequency domain (TFD) components of the mixed signal obtained by ICA. The TFD components are grouped by hierarchical clustering and k-mean partitional clustering. The distance between TFD components is measured with the classical Euclidean distance and the β distance of Gaussian distribution introduced by as. In addition, the TFD components are grouped by minimizing the negentropy of reconstructed constituent signals. The proposed method was used to separate source signals from single audio mixes of two- and three-component signals. The separation was performed using algorithms written by the authors in Matlab. The quality of obtained separation results was evaluated by perceptual tests. The tests showed that the automated separation requires qualitative information about time-frequency characteristics of constituent signals. The best separation results were obtained with the use of the β distance of Gaussian distribution, a distance measure based on the knowledge of the statistical nature of spectra of original constituent signals of the mixed signal.

中文翻译：

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使用基于 ICA 的时频分解进行单通道源分离。


本文涉及通过独立分量分析（ICA）从单个混合信号中分离单通道源信号。所提出的想法在于混合信号的时频表示以及在对应于不同时间间隔的频谱行上使用 ICA。在我们的方法中，为了重建真实源，我们提出了一种新颖的想法，即对 ICA 获得的混合信号的统计独立的时频域 (TFD) 分量进行分组。 TFD 组件通过层次聚类和 k 均值分区聚类进行分组。 TFD分量之间的距离采用经典的欧氏距离和as引入的高斯分布的β距离来测量。此外，通过最小化重建成分信号的负熵来对 TFD 分量进行分组。所提出的方法用于从两分量和三分量信号的单个音频混合中分离源信号。使用作者在 Matlab 中编写的算法进行分离。通过感知测试评估所获得的分离结果的质量。测试表明，自动分离需要有关成分信号的时频特性的定性信息。使用高斯分布的β距离获得了最佳分离结果，这是一种基于混合信号的原始组成信号的频谱统计性质的知识的距离测量。