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International Journal of Social Robotics
Erschienen in: International Journal of Social Robotics 1/2018

13.10.2017

Music to Motion: Using Music Information to Create Expressive Robot Motion

verfasst von: Mathias Sunardi, Marek Perkowski

Erschienen in: International Journal of Social Robotics | Ausgabe 1/2018

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Abstract

In a robot theater, developing high-quality motions for a humanoid robot requires significant time and effort. People with artistic and animation skills are required to create expressive gestures and movements on the robots. We observe that dancers can translate music into motions that are perceived as expressive by the audience. In this study, we developed a program to exploit melodic and dynamics information from music to create expressive robot motion. The program was used on two applications: producing motions strictly from the music information and controlling the execution a pre-programmed sequence of actions. Both applications are intended for a robot theater. The former was applied on an arm robot, while the latter on a humanoid KHR-1 robot. Two surveys were done to analyze the impact of our method. The results suggest that the robot motions produced by our program can be perceived as being more expressive and more dynamic than the motions created by a person or without music information.
Vorheriger Artikel Can Human–Robot Interaction Promote the Same Depth of Social Information Processing as Human–Human Interaction?
Nächster Artikel Mobile Service Robot State Revealing Through Expressive Lights: Formalism, Design, and Evaluation

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Fußnoten
1
The MIDI beat resolution used by MusE is 384 ticks per beat.
 
2
The KHR-1 robot has since been discontinued by its manufacturer. Information about the robot can still be found here: [28].
 
4
The whole music is played for the Mozart Sonata No. 16 because the channel used to generate the motion data does not sound good when played by itself—the main melody is constructed by the combination of multiple channels.
 
Literatur
1.
Amaya K, Bruderlin A, Calvert T (1996) Emotion from motion. In: Graphics interface, vol. 96. Toronto, Canada, pp 222–229
2.
Bhutada A (2011) Universal event and motion editor for robots’ theatre. Master’s thesis, Portland State University, 1900 SW Fourth Ave., Suite 160, Portland, OR 97201
3.
Breazeal C (1999) A context-dependent attention system for a social robot. In: 1999 International joint conference on artificial intelligence
4.
Bruderlin A, Williams L (1995) Motion signal processing. In: Proceedings of the 22nd annual conference on computer graphics and interactive techniques. ACM, pp 97–104
5.
Busso C, Deng Z, Neumann U, Narayanan S (2005) Natural head motion synthesis driven by acoustic prosodic features. Comput Anim Virtual Worlds 16(3–4):283–290CrossRef
6.
Camurri A, Volpe G, Poli GD, Leman M (2005) Communicating expressiveness and affect in multimodal interactive systems. IEEE Multimed 12(1):43–53CrossRef
7.
Fernando R (2004) GPU gems: programming techniques. Tips and tricks for real-time graphics. Pearson Higher Education, Upper Saddle River
8.
Fisher RA (1925) Statistical methods for research workers. Genesis Publishing Pvt Ltd, New DelhiMATH
9.
10.
Hollander M, Wolfe DA, Chicken E (2013) Nonparametric statistical methods. Wiley, HobokenMATH
11.
Johnson R (2014) Choosing between parametric and non-parametric tests. J Undergrad Res Minn State Univ Mankato 9(1):6
12.
Lee D, Park S, Hahn M, Lee N (2014) Robot actors and authoring tools for live performance system. In: 2014 International conference on information science and applications (ICISA). IEEE, pp 1–3
13.
Sergey L (2009) Body language animation synthesis from prosody. Undergraduate honors thesis, Stanford University
14.
15.
McNeill D (1992) Hand and mind: what gestures reveal about thought. University of Chicago Press, Chicago
16.
17.
Meek GE, Ozgur C, Dunning K (2007) Comparison of the t vs. wilcoxon signed-rank test for likert scale data and small samples. J Mod Appl Stat Methods 6(1):10CrossRef
18.
19.
20.
Michalowski MP, Sabanovic S, Kozima H (2007) A dancing robot for rhythmic social interaction. In: 2007 2nd ACM/IEEE international conference on human–robot interaction (HRI). IEEE, pp 89–96
21.
22.
23.
24.
Perkowski M, Bhutada A, Lukac M, Sunardi M (2013) On synthesis and verification from event diagrams in a robot theatre application. In: 2013 IEEE 43rd international symposium on multiple-valued logic (ISMVL). IEEE, pp 77–83
25.
Perkowski M, Sasao T, Kim JH, Lukac M, Allen J, Gebauer S (2005) Hahoe kaist robot theatre: learning rules of interactive robot behavior as a multiple-valued logic synthesis problem. In: 2005. Proceedings. 35th International symposium on multiple-valued logic. IEEE, pp 236–248
26.
Perlin K (1985) An image synthesizer. ACM Siggraph Comput Graphics 19(3):287–296CrossRef
27.
Pilhofer M, Day H (2015) Music theory for dummies. Wiley, Hoboken
28.
29.
Sargin ME, Erzin E, Yemez Y, Tekalp AM, Erdem AT, Erdem C, Ozkan M (2007) Prosody-driven head-gesture animation. In: 2007 IEEE international conference on acoustics, speech and signal processing-ICASSP’07, vol 2. IEEE, pp II–677
30.
Scheeff M, Pinto J, Rahardja K, Snibbe S, Tow R (2002) Experiences with sparky, a social robot. In: Socially intelligent agents. Springer US, pp 173–180
31.
Scheffé H (1952) An analysis of variance for paired comparisons. J Am Stat Assoc 47(259):381–400MathSciNetMATH
32.
Stanislavski C (2013) Building a character. A & C Black, London
33.
Sunardi MI (2010) Expressive motion synthesis for robot actors in robot theatre. Master’s thesis, Portland State University, 1900 SW Fourth Ave., Suite 160, Portland, OR 97201
34.
Thomas F, Johnston O, Thomas F (1995) The illusion of life: disney animation. Hyperion New York, New York
35.
Unuma M, Anjyo K, Takeuchi R (1995) Fourier principles for emotion-based human figure animation. In: Proceedings of the 22nd annual conference on computer graphics and interactive techniques. ACM, pp 91–96
36.
Von Laban R, Lange R (1975) Laban’s principles of dance and movement notation. Princeton Book Co Pub, Princeton
Metadaten
Titel
Music to Motion: Using Music Information to Create Expressive Robot Motion
verfasst von
Mathias Sunardi
Marek Perkowski
Publikationsdatum
13.10.2017
Verlag
Springer Netherlands
Erschienen in
International Journal of Social Robotics / Ausgabe 1/2018
Print ISSN: 1875-4791
Elektronische ISSN: 1875-4805
DOI
https://doi.org/10.1007/s12369-017-0432-9

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