The anthropomorphic flutist robot WF-4R: from mechanical to perceptual improvements

The anthropomorphic flutist robot WF-4R: from mechanical to perceptual improvements
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DOI:
10.1109/iros.2005.1545259
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发表时间:
2005-12
期刊:
2005 IEEE/RSJ International Conference on Intelligent Robots and Systems
影响因子:
--
通讯作者:
J. Solis;K. Chida;S. Isoda;K. Suefuji;C. Arino;A. Takanishi
J. Solis;K. Chida;S. Isoda;K. Suefuji;C. Arino;A. Takanishi
中科院分区:
其他
文献类型:
--
作者:
J. Solis;K. Chida;S. Isoda;K. Suefuji;C. Arino;A. Takanishi

文献摘要

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开发类人机器人的最早动机之一是创造可以在为人类创造的环境中与人类共存的机器人。几年来,在早稻田大学,拟人长笛演奏者的开发一直专注于改善人类和机器人之间的音乐互动,以澄清人类长笛演奏并提出新颖的辅助音乐教学工具。本文介绍了一种使用长笛演奏机器人自主将技能从机器人传递给人类的新体系结构。此外,长笛手机器人的新版本,WF-4 R(早稻田长笛手4号精炼);其中添加了手臂系统,以确保长笛的定位精度和旋律识别系统的开发,使机器人与学生在相同的逻辑水平的感知互动。为了验证机械和感知系统的有效性,已经进行了实验设置。因此,使用手臂系统,我们保证了长笛定位的重复性。此外,所实现的音乐识别系统能够识别长笛演奏者的旋律(总体识别率为90%);这表明HMM(通常用于语音识别)也可有效识别长笛旋律。
One of the earliest motivations for developing humanoid robots centered on creating robots that may coexist with humans in environments created for human beings. For several years, at Waseda University, the development of the anthropomorphic flutist player has been focused on improving the musical interaction between the human and the robot to clarify the human flute playing and to propose novel assisted music teaching tools. In this paper, a new architecture for autonomously transferring skills from robot to human using the flutist robot is introduced. Furthermore, the new version of the flutist robot, the WF-4R (Waseda Flutist No.4 Refined) is presented; where the arm system was added to assure the positioning accuracy of the flute and the development of a melody recognition system to enable the robot to interact with students at the same logical level of perception. An experimental setup has been performed in order to verify the effectiveness of both mechanical and perceptual systems. As a result, using the arms system, we have assured the repetitiveness of the flute positioning. Furthermore, the implemented music recognition system was able to recognize the melody of flutist players (an overall recognition rate of 90%); demonstrating that HMM (usually used for speech recognition) is also effective for flute melody identification.