Probing Aesthetics Strategies for Robot Sound: Complexity and Materiality in Movement Sonification

IF 4.2 Q2 ROBOTICS ACM Transactions on Human-Robot Interaction Pub Date : 2023-03-17 DOI:10.1145/3585277
A. Latupeirissa, C. Panariello, R. Bresin
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引用次数: 3

Abstract

This paper presents three studies where we probe aesthetics strategies of sound produced by movement sonification of a Pepper robot by mapping its movements to sound models. We developed two sets of sound models. The first set was made by two sound models, a sawtooth-based one and another based on feedback chains, for investigating how the perception of synthesized robot sounds would depend on their design complexity. We implemented the second set of sound models for probing the “materiality” of sound made by a robot in motion. This set consisted of a sound synthesis based on an engine highlighting the robot’s internal mechanisms, a metallic sound synthesis highlighting the robot’s typical appearance, and a whoosh sound synthesis highlighting the movement. We conducted three studies. The first study explores how the first set of sound models can influence the perception of expressive gestures of a Pepper robot through an online survey. In the second study, we carried out an experiment in a museum installation with a Pepper robot presented in two scenarios: (1) while welcoming patrons into a restaurant and (2) while providing information to visitors in a shopping center. Finally, in the third study, we conducted an online survey with stimuli similar to those used in the second study. Our findings suggest that participants preferred more complex sound models for the sonification of robot movements. Concerning the materiality, participants liked better subtle sounds that blend well with the ambient sound (i.e., less distracting) and soundscapes in which sound sources can be identified. Also, sound preferences varied depending on the context in which participants experienced the robot-generated sounds (e.g., as a live museum installation vs. an online display).
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探索机器人声音的美学策略:运动声音的复杂性与物质性
本文提出了三项研究,其中我们通过将胡椒机器人的运动映射到声音模型来探索由运动超声产生的声音的美学策略。我们开发了两套声音模型。第一组是由两个声音模型制作的,一个基于锯齿,另一个基于反馈链,用于研究合成机器人声音的感知如何依赖于它们的设计复杂性。我们实现了第二组声音模型,用于探测机器人在运动中发出的声音的“物质性”。这一套包括一个基于引擎的声音合成,突出了机器人的内部机制,一个金属声合成,突出了机器人的典型外观,以及一个嗖嗖声合成,突出了运动。我们进行了三项研究。第一项研究通过在线调查探讨了第一组声音模型如何影响Pepper机器人对表达手势的感知。在第二项研究中,我们在一个博物馆装置中进行了一个实验,让Pepper机器人在两种场景中呈现:(1)在餐厅欢迎顾客时,(2)在购物中心为游客提供信息时。最后,在第三项研究中,我们使用与第二项研究类似的刺激进行了在线调查。我们的研究结果表明,参与者更喜欢更复杂的声音模型来模拟机器人的运动。在物质性方面,参与者喜欢与环境声音(即较少分散注意力)和声源可以识别的音景相融合的更好的细微声音。此外,声音偏好取决于参与者体验机器人生成声音的环境(例如,作为现场博物馆装置还是在线展示)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACM Transactions on Human-Robot Interaction
ACM Transactions on Human-Robot Interaction Computer Science-Artificial Intelligence
CiteScore
7.70
自引率
5.90%
发文量
65
期刊介绍: ACM Transactions on Human-Robot Interaction (THRI) is a prestigious Gold Open Access journal that aspires to lead the field of human-robot interaction as a top-tier, peer-reviewed, interdisciplinary publication. The journal prioritizes articles that significantly contribute to the current state of the art, enhance overall knowledge, have a broad appeal, and are accessible to a diverse audience. Submissions are expected to meet a high scholarly standard, and authors are encouraged to ensure their research is well-presented, advancing the understanding of human-robot interaction, adding cutting-edge or general insights to the field, or challenging current perspectives in this research domain. THRI warmly invites well-crafted paper submissions from a variety of disciplines, encompassing robotics, computer science, engineering, design, and the behavioral and social sciences. The scholarly articles published in THRI may cover a range of topics such as the nature of human interactions with robots and robotic technologies, methods to enhance or enable novel forms of interaction, and the societal or organizational impacts of these interactions. The editorial team is also keen on receiving proposals for special issues that focus on specific technical challenges or that apply human-robot interaction research to further areas like social computing, consumer behavior, health, and education.
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