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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control)
Yoyon K Suprapto
Institut Teknologi Sepuluh Nopember
Computer Science & IT

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Spectral-based Features Ranking for Gamelan Instruments Identification using Filter Techniques Aris Tjahyanto; Yoyon K Suprapto; Diah P Wulandari
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 11, No 1: March 2013
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v11i1.895

Abstract

 In this paper, we describe an approach of spectral-based features ranking for Javanese gamelan instruments identification using filter techniques. The model extracted spectral-based features set of the signal using Short Time Fourier Transform (STFT). The rank of the features was determined using the five algorithms; namely ReliefF, Chi-Squared, Information Gain, Gain Ratio, and Symmetric Uncertainty. Then, we tested the ranked features by cross validation using Support Vector Machine (SVM). The experiment showed that Gain Ratio algorithm gave the best result, it yielded accuracy of 98.93%.
Audio Spike Detection on Gamelan using Envelope Shape Pattern Analysis Solekhan Solekhan; Yoyon K Suprapto; Wirawan Wirawan
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 4: December 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i4.4012

Abstract

Spike detection is important for the analysis of gamelan signal processing, in detecting the onset, transcription, and spikes occur on gamelan percussion instruments. Since the current methods: absolute value and NEO, could not detect spikes properly, we proposed to develop a new spike detection method. In this paper, the early stage of spike detection in gamelan music audio was the determination of the peak signal to obtain the form of a signal pattern (envelope like) which was then used as threshold for determining the spike locations. We also demonstrated the effectiveness of their method using shape pattern analysis to detect spikes.
Saron Music Transcription Based on Rhythmic Information using HMM on Gamelan Orchestra Yoyon K Suprapto; Yosefine Triwidyastuti
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 13, No 1: March 2015
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v13i1.295

Abstract

Nowadays, eastern music exploration is needed to raise his popularity that has been abandoned by the people, especially the younger generation. Onset detection in Gamelan music signals are needed to help beginners follow the beats and the notation. We propose a Hidden Markov Model (HMM) method for detecting the onset of each event in the saron sound. F-measure of average the onset detection was analyzed to generate notations. The experiment demonstrates 97.83% F-measure of music transcription. 
Co-Authors Aris Tjahyanto Diah P Wulandari Solekhan Solekhan Wirawan Wirawan Yosefine Triwidyastuti