Current research in robotic sounds generally focuses on either masking the consequential sound produced by the robot or on sonifying data about the robot to create a synthetic robot sound. We propose to capture, modify, and utilise rather than mask the sounds that robots are already producing. In short, this approach relies on capturing a robot's sounds, processing them according to contextual information (e.g., collaborators' proximity or particular work sequences), and playing back the modified sound. Previous research indicates the usefulness of non-semantic, and even mechanical, sounds as a communication tool for conveying robotic affect and function. Adding to this, this paper presents a novel approach which makes two key contributions: (1) a technique for real-time capture and processing of consequential robot sounds, and (2) an approach to explore these sounds through direct human-robot interaction. Drawing on methodologies from design, human-robot interaction, and creative practice, the resulting 'Robotic Blended Sonification' is a concept which transforms the consequential robot sounds into a creative material that can be explored artistically and within application-based studies.

翻译：当前机器人声音的研究通常聚焦于掩盖机器人产生的声音或对机器人数据进行声化以创建合成机器人声音。我们提出捕捉、修改并利用机器人已产生的声音，而非掩盖它们。简而言之，该方法依赖于捕捉机器人声音，根据上下文信息（例如，协作者的距离或特定工作序列）进行处理，并播放修改后的声音。先前研究表明，非语义甚至机械声音作为传达机器人情感和功能的沟通工具具有实用性。在此基础上，本文提出了一种新颖方法，包含两项关键贡献：（1）一种实时捕捉和处理后果性机器人声音的技术，以及（2）一种通过直接人机交互探索这些声音的方法。借鉴设计、人机交互和创意实践的方法论，由此产生的“机器人混合声化”概念将后果性机器人声音转化为一种创意材料，可在艺术性和应用性研究中进行探索。