基于单纯形分析的数据驱动源分离
Data-Driven Source Separation Based on Simplex Analysis
摘要：
提出了一种基于概率模型的数据驱动的盲源分离方法。所提出的方法是专门设计用于分离多声道音频混合。该算法依赖于不同时间帧之间的相关矩阵的谱分解。概率模型意味着相关矩阵的列空间由各个说话者在时间上的概率所跨越。通过特征值衰减来恢复说话人的数目，并且特征向量形成说话人概率的单纯形。时间帧占主导地位的每个扬声器确定利用凸几何工具恢复的单纯形。利用所识别的帧集合来估计混合声学通道，并且执行线性混合以提取各个扬声器。派生的单形直观地证明了2，3和4扬声器的混合物。我们还进行了全面的实验研究，在各种混响条件下显示出高的分离能力。
英文摘要：
Blind source separation (BSS) is addressed, using a novel data-driven approach, based on a well-established probabilistic model. The proposed method is specifically designed for separation of multichannel audio mixtures. The algorithm relies on spectral decomposition of the correlation matrix between different time frames. The probabilistic model implies that the column space of the correlation matrix is spanned by the probabilities of the various speakers across time. The number of speakers is recovered by the eigenvalue decay, and the eigenvectors form a simplex of the speakers' probabilities. Time frames dominated by each of the speakers are identified exploiting convex geometry tools on the recovered simplex. The mixing acoustic channels are estimated utilizing the identified sets of frames, and a linear umixing is performed to extract the individual speakers. The derived simplexes are visually demonstrated for mixtures of 2, 3 and 4 speakers. We also conduct a comprehensive experimental study, showing high separation capabilities in various reverberation conditions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
