Real-Time Tracking of Multiple Sound Sources by Integration of In-Room and Robot-Embedded Microphone Arrays
Real-Time Tracking of Multiple Sound Sources by Integration of In-Room and Robot-Embedded Microphone Arrays
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DOI：
10.1109/iros.2006.281737
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发表时间：
2006-10
期刊：
影响因子：
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通讯作者：
K. Nakadai;H. Nakajima;M. Murase;HIroshi G. Okuno;Yuji Hasegawa;H. Tsujino
中科院分区：
文献类型：
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作者：
K. Nakadai;H. Nakajima;M. Murase;HIroshi G. Okuno;Yuji Hasegawa;H. Tsujino
实时和稳健的声源跟踪对于在日常环境中操作的机器人来说是一项重要的功能，因为机器人应该识别语音、音乐和其他环境声音等声音事件的来源。本文研究了室内麦克风阵列(IRMA)和机器人嵌入麦克风阵列(REMA)实时集成的声源跟踪方法。IRMA系统由固定在墙上的ch麦克风组成。它基于2D平面上的加权延迟和波束形成来定位多个声源。REMA系统使用8个麦克风在转台上连接到机器人头部，从而在方位上定位多个声源。通过使用粒子滤波来集成定位结果以实时跟踪多个声源。实验结果表明，即使在机器人头部旋转的情况下，基于粒子滤波的融合算法也提高了多声源跟踪的精度和鲁棒性
Real-time and robust sound source tracking is an important function for a robot operating in a daily environment, because the robot should recognize where a sound event such as speech, music and other environmental sounds originate from. This paper addresses real-time sound source tracking by real-time integration of an in-room microphone array (IRMA) and a robot-embedded microphone array (REMA). The IRMA system consists of 64 ch microphones attached to the walls. It localizes multiple sound sources based on weighted delay-and-sum beam-forming on a 2D plane. The REMA system localizes multiple sound sources in azimuth using eight microphones attached to a robot's head on a rotational table. The localization results are integrated to track multiple sound sources by using a particle filter in real-time. The experimental results show that particle filter based integration improved accuracy and robustness in multiple sound source tracking even when the robot's head was in rotation