Permutation Correction and Speech Extraction Based on Split Spectrum Through FastICA
Permutation Correction and Speech Extraction Based on Split Spectrum Through FastICA
复制标题
通过FastICA进行基于分裂谱的排列校正和语音提取
DOI：
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发表时间：
2003
期刊：
影响因子：
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通讯作者：
N. Haratani
中科院分区：
文献类型：
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作者：
H. Gotanda;Kazuyuki Nobu;T. Koya;Kei;T. Ishibashi;N. Haratani
利用分裂谱的显著特征和信号源的位置信息，提出了一种无排列和尺度不确定性的盲源反褶积方法。利用FastICA的非高斯特性将源信号按大的非高斯性顺序从混合信号中分离出来，以及人类语音的非高斯性通常大于噪声这一事实，提出了一种专门提取人类语音的方法。在真实的房间中进行的实验验证了所提出的方法。
A blind source deconvolution method without indeterminacy of permutation and scaling is proposed by using notable features of split spectrum and locational information on signal sources. A method for extracting human speech exclusively is also proposed by taking advantage of the rule, the property of FastICA separates sources in order of large non-Gaussianity from their mixtures and the fact that human speeches are usually larger in non-Gaussianity than noises. The proposed methods have been veried by several experiments in a real room.