| 基于高斯混合密度函数估计的语音分离 |
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| 引用本文： | 虞晓,胡光锐. 基于高斯混合密度函数估计的语音分离[J]. 上海交通大学学报, 2000, 34(2): 177-180 |
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| 作者姓名： | 虞晓 胡光锐 |
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| 作者单位： | 上海交通大学,电子工程系,上海,200030 |
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| 基金项目： | 国家自然科学基金资助项目(69672007) |
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| 摘 要： | 基于最大熵法（Ｍａｘｉｍｕｍ Ｅｎｔｒｏｐｙ，ＭＥ）、最小互信息量法（Ｍｉｎｉｍｕｍ Ｍｕｔｕａｌ Ｉｎｆｏｒｍａｔｉｏｎ，ＭＭＩ）和最大似然法（ＭａｘｉｍｕｍＬＩｋｅｌｉｈｏｏｄ，ＭＬ）最解决盲信号分离问题的常用算法，分析了ＭＥ、ＭＭＩ以及ＭＬ算法之间关系。基于高斯混合模式（Ｇａｕｓｓｉａｎ Ｍｉｘｔｕｒｅ Ｍｏｄｅｌ，ＧＭＭ）概率密度函数估计，提出了一种采用反馈结构的扩展最大熵语音分离算法，与
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| 关 键 词： | 语音分离 盲信号分离 高斯混合模式 密度函数 |
| 文章编号： | 1006-2467(2000)02-0177-04 |
| 修稿时间： | 1998-06-19 |
Speech Separation Based on Gaussian Mixture Model Probability Density Function Estimation |
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| YU Xiao,HU Guang-rui. Speech Separation Based on Gaussian Mixture Model Probability Density Function Estimation[J]. Journal of Shanghai Jiaotong University, 2000, 34(2): 177-180 |
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| Authors: | YU Xiao HU Guang-rui |
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| Abstract: | The speech separation task was seen as a convolution mixture blind signal separation (BSS) problem. There are 3 kinds of main approaches to solve the BSS problem: ME(maximum entropy) algorithm, MMI(minimum mutual information) algorithm, and ML (maximum likelihood) algorithm. The relationship among the 3 kinds of algorithms was analyzed in this paper. Based on the feedback architecture and Gaussian mixture model (GMM) probability density function (pdf) estimation, a new extended ME algorithm speech separation algorithm was proposed. Based on the computer simulations of the proposed algorithm and traditional ME algorithm, it can be concluded that the proposed algorithm has better convergence performance. |
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| Keywords: | speech separation blind signal separation (BSS) Gaussian mixture model (GMM) feedback architecture |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
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