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Multiple Sound Sources Localization with Frame-by-Frame Component Removal of Statistically Dominant Source
Maoshen Jia1, Yuxuan Wu2, Changchun Bao3
1Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China. jiamaoshen@bjut.edu.cn.
Sensors (Basel, Switzerland)
|October 26, 2018
Summary
This study introduces a novel method for pinpointing multiple sound sources using a statistically dominant source component removal (SDSCR) algorithm. The SDSCR algorithm enhances sound source localization accuracy by effectively removing unwanted noise while preserving weaker signals.
Area of Science:
- Audio Signal Processing
- Acoustics
- Machine Learning
Background:
- Multiple sound source localization is crucial for various applications.
- Existing methods face challenges with statistically weak sources (SWS) degrading accuracy.
Purpose of the Study:
- To propose a novel sound source localization method using a soundfield microphone.
- To address the degradation of localization accuracy caused by statistically weak sources.
Main Methods:
- Development of a statistically dominant source component removal (SDSCR) algorithm.
- Utilizing joint intra-frame and inter-frame statistically dominant source (SDS) discriminations.
- Validation of SWS existence through statistical analysis.
Main Results:
- The SDSCR algorithm effectively removes SDS components while preserving SWS components.
- The proposed method resolves localization accuracy degradation caused by SWS.
- Objective evaluations in simulated and real environments demonstrate superior performance.
Conclusions:
- The proposed SDSCR method significantly improves multiple sound source localization and counting.
- It outperforms conventional SSZ-based methods in both accuracy and source identification.
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