基于压缩感知的双麦克风混响多声源定位算法

张奕; 李娟; 张敏

doi:10.11959/j.issn.1000-436x.2019011

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基于压缩感知的双麦克风混响多声源定位算法

学术论文 | 更新时间：2024-06-05

- 基于压缩感知的双麦克风混响多声源定位算法
- Reverberation multi-source localization algorithm based on compressed sensing with dual microphones
- 通信学报 2019年40卷第1期页码：102-109
- 作者机构：
  
  大连大学信息工程学院，辽宁大连 116622
- 作者简介：
  
  [ "张奕（1978- ），男，山东济南人，博士，大连大学副教授，主要研究方向为语音信号处理、阵列信号处理、进化算法和压缩感知。" ]
  [ "李娟（1993- ），女，甘肃会宁人，大连大学硕士生，主要研究方向为阵列信号处理。" ]
  [ "张敏（1966- ），女，辽宁大连人，博士，大连大学副教授，主要研究方向为大数据挖掘、生物信息、智能算法。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61201420)
- DOI：10.11959/j.issn.1000-436x.2019011
  中图分类号： TN912.3
- 网络出版日期：2019-01，
  
  纸质出版日期：2019-01-25
- 稿件说明：
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张奕, 李娟, 张敏. 基于压缩感知的双麦克风混响多声源定位算法[J]. 通信学报, 2019,40(1):102-109.

Yi ZHANG, Juan LI, Min ZHANG. Reverberation multi-source localization algorithm based on compressed sensing with dual microphones[J]. Journal on communications, 2019, 40(1): 102-109.
张奕, 李娟, 张敏. 基于压缩感知的双麦克风混响多声源定位算法[J]. 通信学报, 2019,40(1):102-109. DOI： 10.11959/j.issn.1000-436x.2019011.

Yi ZHANG, Juan LI, Min ZHANG. Reverberation multi-source localization algorithm based on compressed sensing with dual microphones[J]. Journal on communications, 2019, 40(1): 102-109. DOI： 10.11959/j.issn.1000-436x.2019011.

摘要

针对混响条件下现有声源定位技术中麦克风数量必须大于声源数量的现状，提出了一种基于压缩感知的双麦克风混响多声源（至少3个声源）定位算法。将多声源定位问题看作是块稀疏信号的重构问题，在频域将全房间冲激响应归一化来构造压缩观测矩阵，重构的块稀疏信号中非零块的位置即对应了空间中实际声源的位置。仿真实验表明，与基于子带可控响应功率（SRP-sub）的多声源定位方法相比，在双麦克风混响条件下定位多声源，基于压缩感知的多声源定位算法的定位性能更高，在混响时间为0.6 s时，仅采用40个频点值，定位3个声源的成功率可以达到80%。

Abstract

In traditional multi-source localization field

it is necessary to guarantee that the number of microphone is more than the number of source.To overcome this constraint

a dual-microphone multi-source localization algorithm based on CS was proposed

where the number of sound source localized successfully was more than 3.The multi-source localization was regarded as the block sparse signal reconstruction in this algorithm

and the full room impulse responses normalized were exploited to construct the compressed observation matrix in frequency domain.In reconstructed block sparse signal

the positions of non-zero blocks were corresponded to the positions of sound sources in space.The simulation shows that compared with the SRP-sub algorithm

in reverberation time 0.6s with dual-microphone

the proposed multi-source localization algorithm based on compressed sensing has higher capability which can reach 80% success rate by using 40 frequency points to localize 3 sound sources.

关键词

Keywords

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