TY - JOUR
KW - Intangible cultural heritages
KW - Intangible culture
KW - machine learning
KW - Learning systems
KW - Pattern recognition
KW - Support vector machines
KW - Robots
KW - Intelligent machine
KW - Multi-class support vector machines
KW - Speech recognition
KW - Extracted energies
KW - Mel-frequency cepstral coefficients
KW - Music information retrieval
KW - Puppet theaters
KW - Wayang kulit
AU - Tito Tomo
AU - Alexander Schmitz
AU - Guillermo Enriquez
AU - Shuji Hashimoto
AU - Shigeki Sugano
AB - This paper proposes a way to protect endangered wayang puppet theater, an intangible cultural heritage from Indonesia, by turning a robot into a puppeteer successor. We developed a seven degrees-offreedom (DOF) manipulator to actuate the sticks attached to the wayang puppet body and hands. The robot can imitate 8 distinct human puppeteer’smanipulations. Furthermore, we developed a gamelan music pattern recognition, towards a robot that can perform based on the gamelan music. In the offline experiment, we extracted energy (time domain), spectral rolloff, 13 Mel-frequency cepstral coefficients (MFCCs), and the harmonic ratio from 5 s long clips, every 0.025 s, with a window length of 1 s, for a total of 2576 features. Two classifiers (3 layers feed-forward neural network (FNN) and multi-class Support Vector Machine (SVM)) were compared. The SVMclassifier outperformed the FNN classifier with a recognition rate of 96.4\%for identifying the three different gamelan music patterns.
BT - Journal of Robotics and Mechatronics
DA - feb
DO - 10.20965/jrm.2017.p0137
LA - English
M1 - 1
N1 - Publisher: Fuji Technology Press
N2 - This paper proposes a way to protect endangered wayang puppet theater, an intangible cultural heritage from Indonesia, by turning a robot into a puppeteer successor. We developed a seven degrees-offreedom (DOF) manipulator to actuate the sticks attached to the wayang puppet body and hands. The robot can imitate 8 distinct human puppeteer’smanipulations. Furthermore, we developed a gamelan music pattern recognition, towards a robot that can perform based on the gamelan music. In the offline experiment, we extracted energy (time domain), spectral rolloff, 13 Mel-frequency cepstral coefficients (MFCCs), and the harmonic ratio from 5 s long clips, every 0.025 s, with a window length of 1 s, for a total of 2576 features. Two classifiers (3 layers feed-forward neural network (FNN) and multi-class Support Vector Machine (SVM)) were compared. The SVMclassifier outperformed the FNN classifier with a recognition rate of 96.4\%for identifying the three different gamelan music patterns.
PY - 2017
SP - 137
EP - 145
T2 - Journal of Robotics and Mechatronics
TI - Wayang Robot with Gamelan Music Pattern Recognition
UR - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85013880391&doi=10.20965%2fjrm.2017.p0137&partnerID=40&md5=4ca47c7a752abe03a80b8599ff3bc2c2
VL - 29
SN - 09153942 (ISSN)
ER -