Acoustic Signal Processing and Scene Analysis for Socially Assistive Robots

用户和机器人之间的交互通常发生在繁忙的环境中，存在相互竞争的扬声器和电视等背景噪声源。因此，机器人的麦克风接收到的信号混合了来自多个声源的信号、环境噪声和声波反射引起的混响。因此，为了专注于感兴趣的刺激，机器人必须学习和适应声环境。这项研究的目的是为机器人和机器提供理解和适应周围声学环境的能力。声学场景分析结合了观察到的音频信号的显著特征，以创建环境的态势感知；声源可以被检测、定位和识别，同时房间本身的声学特性也可以被表征。利用通过分析声音场景获得的信息，可以创建环境的三维地图，并可用于识别声音或识别语音信号的意图。此外，通过在环境中移动，机器人可以探索和了解周围环境的声学特性。然而，声学场景分析所需的许多任务是共同依赖的。例如，定位隐藏在噪音和混响中的声源是一个具有挑战性的问题。声源定位可以通过增强所需声源（如人类说话者）的信号来改进，同时抑制干扰源（如电视）。然而，对于源增强，必须在空间上区分期望源和干扰源，因此需要了解源方向。因此，本研究的新目标是识别和建设性地利用声场景分析所需任务之间的联合依赖关系。为了实现这一目标，该项目将利用机器人的运动，以便从不同的角度看待不确定事件。通过将安装在机器人四肢上的麦克风与安装在机器人头部的麦克风阵列相融合，将开发技术来建设性地利用机器人手臂的运动。此外，还将研究允许多个机器人共享声学环境经验和知识的方法。该研究将在伦敦帝国理工学院电气与电子工程系进行，并得到英国爱丁堡大学的国家、欧洲和国际项目合作伙伴的学术建议；德国埃尔兰根国际音频实验室；以色列巴伊兰大学。

The interaction between users and a robot often takes place in busy environments in the presence of competing speakers and background noise sources such as televisions. The signals received at the microphones of the robot are hence a mixture of the signals from multiple sound sources, ambient noise, and reverberation due to reflections of sound waves. Thus, in order to focus on stimuli of interest, the robot has to learn and adapt to the acoustic environment.The aim of this research is to provide robots and machines with the ability to understand and adapt to the surrounding acoustic environment. Acoustic scene analysis combines salient features from the observed audio signals in order to create situational awareness of the environment; Sound sources are detected, localised and identified, whilst acoustic properties of the room itself can be characterised. Using the information acquired by analysing the acoustic scene, a three-dimensional map of the environment is created, and can be used to identify sounds or recognise the intent of speech signals. Moreover, by moving within the environment, the robot can explore and learn about the acoustic properties of its surrounding. However, many of the tasks required for analysis of the acoustic scene are jointly dependent. For example, localising the sources of sounds buried in noise and reverberation is a challenging problem. Sound source localisation can be improved by enhancing the signals of desired sources, such as human speakers, whilst suppressing interfering sources, such as a television. However, for source enhancement, desired and interfering sources must be spatially distinguished, hence requiring knowledge of the source directions. The novel objective of this research is therefore to identify and exploit constructively the joint dependencies between the tasks required for acoustic scene analysis. To achieve this objective, the project will take advantage of the motion of the robot in order to look at uncertain events from different perspectives. Techniques will be developed to constructively exploit motion of the robot's arms by fusing microphones attached to the robot's limbs with microphone arrays installed in the robot head. Furthermore, approaches will be investigated that allow multiple robots to share their experience and knowledge about the acoustic environment.The research will be conducted at Imperial College London, within the Department of Electrical and Electronic Engineering with academic advice from national, European, and international project partners at the University of Edinburgh, UK; International Audio Laboratories Erlangen, Germany; and Bar-Ilan University, Israel.