Blind Source Separation Based on Local Generalized Gaussian Mixture Model

被引：0

作者：

Chen, Yongqiang ^{[1
]}

机构：

[1] Chengdu Univ Informat Technol, Coll Commun Engn, Chengdu, Sichuan, Peoples R China

来源：

2017 IEEE 9TH INTERNATIONAL CONFERENCE ON COMMUNICATION SOFTWARE AND NETWORKS (ICCSN) | 2017年

关键词：

underdetermined blind source separation; local generalized gaussian mixture model; maximum posterior probability;

D O I：

暂无

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

In this paper, the local generalized Gaussian mixture model (LGGMM) is proposed to blindly separate speech signals in reverberation environment. First, time delay and attenuation ratio of speech signal to the microphone array are estimated by detection of single source points. Second, by using LGGMM to determine the dominant sources at every time-frequency point, the speech signals are separated. Experiment simulation results show that this method can further improve judgment accuracy of the dominant sources and can better separate the speech signals even if reverberation time is longer.

引用

页码：935 / 938

页数：4

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