02135nas a2200373   4500000000100000000000100001008004100002653002300043653003400066653002300100653002400123653002100147653002100168653004000189653004000229653003200269653002400301653002000325653001100345653002300356653002800379653001700407100001400424700002200438700002300460700002000483700001900503245005600522856014800578300001200726490000700738520099600745022002001741                                       d10aExtracted energies10aIntangible cultural heritages10aIntangible culture10aIntelligent machine10aLearning systems10amachine learning10aMel-frequency cepstral coefficients10aMulti-class support vector machines10aMusic information retrieval10aPattern recognition10aPuppet theaters10aRobots10aSpeech recognition10aSupport vector machines10aWayang kulit1 aTito Tomo1 aAlexander Schmitz1 aGuillermo Enriquez1 aShuji Hashimoto1 aShigeki Sugano00aWayang Robot with Gamelan Music Pattern Recognition  uhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85013880391&doi=10.20965%2fjrm.2017.p0137&partnerID=40&md5=4ca47c7a752abe03a80b8599ff3bc2c2  a137-1450 v293 aThis 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.  a09153942 (ISSN)