| 改进的独立分量分析在语音分离中的应用 |
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| 引用本文： | 陈艳.改进的独立分量分析在语音分离中的应用[J].电子科技,2009,22(12):83-87. |
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| 作者姓名： | 陈艳 |
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| 作者单位： | 池州学院,物理与机电工程系,安徽,池州,247000 |
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| 摘 要： | 对于自适应盲分离算法,选择步长参数以达到理想的分离性能是必要的。结合基于估计函数的可变步长自适应优化方法,提出了基于负熵的自适应算法,有效提高了算法的收敛速度,降低了算法性能对步长的依赖性。仿真实验证明了此改进算法具有较好的分离效果。
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| 关 键 词： | 独立分量分析 盲源分离 可变步长 |
Application of Improved Independent Component Analysis Technology in Speech Signal Separation |
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| Chen Yan.Application of Improved Independent Component Analysis Technology in Speech Signal Separation[J].Electronic Science and Technology,2009,22(12):83-87. |
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| Authors: | Chen Yan |
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| Affiliation: | Chen Yan(Department of Physics and Mechano-elcetronic,Chizhou Collage,Chizhou 247000,Chian) |
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| Abstract: | Careful selection of step size parameters is often necessary to obtain good performance from gradient-based adaptive algorithms for blind source separation.Combining an adaptive variable step-size based on the estimated function,a new adaptive algorithm based on negative entropy is given.Computer simulation results show that this improved adaptive algorithm has better separation efficiency and fast convergence. |
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| Keywords: | independent component analysis blind signal processing variable step-size |
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