Robot audition for dynamic environments
Robot audition for dynamic environments
复制标题
动态环境的机器人试镜
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
H. Nakajima
中科院分区：
文献类型：
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作者：
K. Nakadai;G. Ince;K. Nakamura;H. Nakajima
本文讨论了机器人试听的动态环境中，扬声器和/或机器人是动态变化的声学环境中移动。目前研究的机器人试听都是假设静态的人机交互场景，难以适应动态环境。我们最近开发了新技术，让机器人即使在动态环境中也能用自己的耳朵同时听几件事;基于广义特征值分解的多信号分类（GEVD-MUSIC），基于几何约束的高阶解相关的自适应步长控制源分离（GHDSS-AS），基于直方图的递归水平估计（HRLE）和基于模板的自我噪声抑制（TENS）。GEVD-MUSIC提供噪声鲁棒的声源定位。GHDSS-AS是一种新的声源分离方法，其声源分离参数能快速适应动态变化。HRLE是一种实用的后滤波方法，具有少量的参数。ENS使用预先记录的模板估计机器人的电机噪声并消除它。这些方法被实现为我们的开源机器人试听软件HARK的模块，以便轻松集成。我们表明，这些方法及其组合是有效的，以科普动态环境，通过离线实验和在线实时演示。
This paper addresses robot audition for dynamic environments, where speakers and/or a robot is moving within a dynamically-changing acoustic environment. Robot Audition studied so far assumed only stationary human-robot interaction scenes, and thus they have difficulties in coping with such dynamic environments. We recently developed new techniques for a robot to listen to several things simultaneously using its own ears even in dynamic environments; MUltiple SIgnal Classification based on Generalized Eigen-Value Decomposition (GEVD-MUSIC), Geometrically constrained High-order Decorrelation based Source Separation with Adaptive Step-size control (GHDSS-AS), Histogram-based Recursive Level Estimation (HRLE), and Template-based Ego Noise Suppression (TENS). GEVD-MUSIC provides noise-robust sound source localization. GHDSS-AS is a new sound source separation method which quickly adapts its sound source separation parameters to dynamic changes. HRLE is a practical post-filtering method with a small number of parameters. ENS estimates the motor noise of the robot by using templates recorded in advance and eliminates it. These methods are implemented as modules for our open-source robot audition software HARK to be easily integrated. We show that each of these methods and their combinations are effective to cope with dynamic environments through off-line experiments and on-line real-time demonstrations.