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基于联合对角化的声信号深度卷积混合盲分离方法

李扬 张伟涛 楼顺天

引用本文: 李扬, 张伟涛, 楼顺天. 基于联合对角化的声信号深度卷积混合盲分离方法[J]. 电子与信息学报, doi: 10.11999/JEIT190067 shu
Citation:  Yang LI, Weitao ZHANG, Shuntian LOU. Deep Convolution Blind Separation of Acoustic Signals Based on Joint Diagonalization[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT190067 shu

基于联合对角化的声信号深度卷积混合盲分离方法

    作者简介: 李扬: 男,1987年生,博士生,研究方向为盲信号处理;
    张伟涛: 男,1983年生,副教授,硕士生导师,研究方向为盲信号处理、语音信号处理;
    楼顺天: 男,1962年生,教授,博士生导师,研究方向为神经网络信息处理与应用、模糊信息处理与应用、盲信号处理、现代信号智能处理、智能控制技术;
    通讯作者: 张伟涛, zhwt-work@foxmail.com
  • 基金项目: 国家自然科学基金(61571339),陕西省创新人才推进计划-青年科技新星项目(2018KJXX-019)

摘要: 声信号在空间中的传播具有较强的多径效应,在接收端往往以卷积形式相互叠加,尤其在海洋、剧场等强混响条件下,混合滤波器冲击响应的长度会显著增加,现有的频域卷积盲分离算法将失效。为了消除长脉冲响应导致解混合模型失效的问题,该文对观测信号进行两次短时傅里叶变换(STFT),第1次STFT缩短了脉冲响应长度,第2次STFT将信号模型转化为瞬时盲分离,最终利用联合对角化技术估计出分离矩阵。与现有方法相比,所提方法解决了深度卷积混合下模型失效的问题,并且当源信号数较多或存在加性噪声时,可以得到更好的分离性能。仿真结果验证了方法的有效性和性能优势。

English

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  • 图 1  算法性能比较

    图 2  声音信号时域波形

    表 1  不同源信号数情况下调整排序混乱前后SIR对比(dB)

    分离性能信源个数
    234
    调序前3.41.51.2
    调序后15.414.914.3
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文章相关
  • 通讯作者:  张伟涛, zhwt-work@foxmail.com
  • 收稿日期:  2019-01-24
  • 网络出版日期:  2019-06-24
通讯作者: 陈斌, bchen63@163.com
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