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Multi-shift principal component analysis based primary component extraction for spatial audio reproduction

机译:基于多移位主成分分析的主成分提取,用于空间音频再现

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In spatial audio analysis-synthesis, one of the key issues is to decompose a signal into primary and ambient components based on their spatial features. Principal component analysis (PCA) has been widely employed in primary component extraction, and shifted PCA (SPCA) is employed to enhance the primary extraction for input signals involving the inter-channel time difference. However, SPCA generally requires the primary components to come from one direction and cannot produce good results in the case of multiple directions. To solve this problem, we propose multi-shift PCA (MSPCA) by extending SPCA to multiple shifts. Two structures of MSPCA with different weighting methods are discussed. From the results of our simulations and listening tests, the proposed consecutive MSPCA with proper weighting is found to be superior to the conventional PCA and SPCA based primary extraction methods.
机译:在空间音频分析合成中,关键问题之一是根据信号的空间特征将信号分解为主要和周围成分。主成分分析(PCA)已广泛用于主成分提取中,而移位PCA(SPCA)用于增强涉及通道间时间差的输入信号的主提取。但是,SPCA通常要求主要成分来自一个方向,并且在多个方向上无法产生良好的效果。为了解决此问题,我们通过将SPCA扩展到多个班次提出了多班次PCA(MSPCA)。讨论了具有不同加权方法的MSPCA的两种结构。从我们的模拟和听觉测试的结果中,发现具有适当权重的拟议连续MSPCA优于传统的基于PCA和SPCA的主要提取方法。

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