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Acoustic Self-Awareness of Autonomous Systems in a World of Sounds
Proceedings of the IEEE ( IF 23.2 ) Pub Date : 2020-07-01 , DOI: 10.1109/jproc.2020.2977372
Alexander Schmidt , Heinrich W. Lollmann , Walter Kellermann

Autonomous systems (ASs) operating in real-world environments are exposed to a plurality and diversity of sounds that carry a wealth of information for perception in cognitive dynamic systems. While the importance of the acoustic modality for humans as “ASs” is obvious, it is investigated to what extent current technical ASs operating in scenarios filled with airborne sound exploit their potential for supporting self-awareness. As a first step, the state of the art of relevant generic techniques for acoustic scene analysis (ASA) is reviewed, i.e., source localization and the various facets of signal enhancement, including spatial filtering, source separation, noise suppression, dereverberation, and echo cancellation. Then, a comprehensive overview of current techniques for ego-noise suppression, as a specific additional challenge for ASs, is presented. Not only generic methods for robust source localization and signal extraction but also specific models and estimation methods for ego-noise based on various learning techniques are discussed. Finally, active sensing is considered with its unique potential for ASA and, thus, for supporting self-awareness of ASs. Therefore, recent techniques for binaural listening exploiting head motion, for active localization and exploration, and for active signal enhancement are presented, with humanoïd robots as typical platforms. Underlining the multimodal nature of self-awareness, links to other modalities and nonacoustic reference information are pointed out where appropriate.

中文翻译:

声音世界中自主系统的声学自我意识

在现实世界环境中运行的自治系统 (AS) 会接触到多种声音,这些声音携带着认知动态系统中用于感知的丰富信息。虽然作为“AS”的人类声学模式的重要性是显而易见的,但研究了当前在充满空气声的场景中运行的技术 AS 在多大程度上利用了它们支持自我意识的潜力。作为第一步,回顾了用于声学场景分析 (ASA) 的相关通用技术的最新技术,即源定位和信号增强的各个方面,包括空间滤波、源分离、噪声抑制、去混响和回声消除。然后,对当前自我噪声抑制技术的全面概述,作为对 AS 的特定额外挑战,被表达。不仅讨论了鲁棒源定位和信号提取的通用方法,而且讨论了基于各种学习技术的自我噪声的特定模型和估计方法。最后,主动感知被认为具有 ASA 的独特潜力,因此支持 AS 的自我意识。因此,提出了利用头部运动进行双耳聆听、主动定位和探索以及主动信号增强的最新技术,其中以类人机器人为典型平台。强调自我意识的多模态性质,在适当的地方指出了与其他模态和非声学参考信息的链接。不仅讨论了鲁棒源定位和信号提取的通用方法,而且讨论了基于各种学习技术的自我噪声的特定模型和估计方法。最后,主动感知被认为具有 ASA 的独特潜力,因此支持 AS 的自我意识。因此,提出了利用头部运动进行双耳聆听、主动定位和探索以及主动信号增强的最新技术,其中以类人机器人为典型平台。强调自我意识的多模态性质,在适当的地方指出了与其他模态和非声学参考信息的链接。不仅讨论了鲁棒源定位和信号提取的通用方法,而且讨论了基于各种学习技术的自我噪声的特定模型和估计方法。最后,主动感知被认为具有 ASA 的独特潜力,因此支持 AS 的自我意识。因此,提出了利用头部运动进行双耳聆听、主动定位和探索以及主动信号增强的最新技术,其中以类人机器人为典型平台。强调自我意识的多模态性质,在适当的地方指出了与其他模态和非声学参考信息的链接。介绍了利用头部运动进行双耳聆听、主动定位和探索以及主动信号增强的最新技术,其中以类人机器人为典型平台。强调自我意识的多模态性质,在适当的地方指出了与其他模态和非声学参考信息的链接。介绍了利用头部运动进行双耳聆听、主动定位和探索以及主动信号增强的最新技术,其中以类人机器人为典型平台。强调自我意识的多模态性质,在适当的地方指出了与其他模态和非声学参考信息的链接。
更新日期:2020-07-01
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