Symphony: Localizing Multiple Acoustic Sources with a Single Microphone Array

被引:40
作者
Wang, Weiguo [1 ,2 ]
Li, Jinming [1 ,2 ]
He, Yuan [1 ,2 ]
Liu, Yunhao [1 ,2 ,3 ]
机构
[1] Tsinghua Univ, Sch Software, Beijing, Peoples R China
[2] Tsinghua Univ, BNRist, Beijing, Peoples R China
[3] Michigan State Univ, Dept Comp Sci & Engn, E Lansing, MI 48824 USA
来源
PROCEEDINGS OF THE 2020 THE 18TH ACM CONFERENCE ON EMBEDDED NETWORKED SENSOR SYSTEMS, SENSYS 2020 | 2020年
基金
国家重点研发计划;
关键词
Voice Assistant; Multi-Source Localization; Microphone Array; LOCALIZATION;
D O I
10.1145/3384419.3430724
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Sound recognition is an important and popular function of smart devices. The location of sound is basic information associated with the acoustic source. Apart from sound recognition, whether the acoustic sources can be localized largely affects the capability and quality of the smart device's interactive functions. In this work, we study the problem of concurrently localizing multiple acoustic sources with a smart device (e.g., a smart speaker like Amazon Alexa). The existing approaches either can only localize a single source, or require deploying a distributed network of microphone arrays to function. Our proposal called Symphony is the first approach to tackle the above problem with a single microphone array. The insight behind Symphony is that the geometric layout of microphones on the array determines the unique relationship among signals from the same source along the same arriving path, while the source's location determines the DoAs (direction-of-arrival) of signals along different arriving paths. Symphony therefore includes a geometry-based filtering module to distinguish signals from different sources along different paths and a coherence-based module to identify signals from the same source. We implement Symphony with different types of commercial off-the-shelf microphone arrays and evaluate its performance under different settings. The results show that Symphony has a median localization error of 0.694m, which is 68% less than that of the state-of-the-art approach.
引用
收藏
页码:82 / 94
页数:13
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