Wavelet Transformation and Spectral Subtraction Method in Performing Automated Rindik Song Transcription

Rindik is Balinese traditional music consisting of bamboo rods arranged horizontally and played by hitting the rods with a mallet-like tool called "panggul". In this study, the transcription of Rindik's music songs was carried out automatically using the Wavelet transformation method and spectral subtraction. Spectral subtraction method is used with iterative estimation and separation approaches. While the Wavelet transformation method is used by matching the segment Wavelet results with the Wavelet result references in the dataset. The results of the transcription were also synthesized again using the concatenative synthesis method. The data used is the hit of 1 Rindik rod and a combination of 2 Rindik rods that are hit simultaneously, and for testing the system, 4 Rindik songs are used. Each data was recorded 3 times. Several parameters are used for the Wavelet transformation method and spectral subtraction, which are the length of the frame for the Wavelet transformation method and the tolerance interval for frequency difference in spectral subtraction method. The test is done by measuring the accuracy of the transcription from the system within all Rindik song data. As a result, the Wavelet transformation method produces an average accuracy of 83.42% and the spectral subtraction method produces an average accuracy of 78.51% in transcription of Rindik songs

NEURAL NETWORK BACKPROPAGATION FOR KENDANG TUNGGAL TONE CLASSIFICATION

Kendang Bali is one of the instruments incorporated in this karawitan art. Balinese kendang can be played alone, called a kendang tunggal, where this type of game has a high level of difficulty understanding the tone of the Balinese drums played because some variations of the tone have similar sounds to other tones. Knowing the tone that is in the kendang song automatically can make it easier to learn it. The first approach method used to classify the tone of a kendang tunggal song is segmentation. The onset detection method is used to segment a kendang song with a variation of the hop size parameter. The segmented tone of the punch will be classified using the Backpropagation method. Feature values of autocorrelation, ZCR, STE, RMSE, Spectral Contrast, MFCC, and Mel spectrogram will be used in the classification process. This study performed variations in hop size values in onset detection and obtained the proper configuration at a value of 110. The addition of the normalization process to the onset detection method also helps the segmentation process of kendang songs correctly. The optimal backpropagation architecture obtained is learning rate 0.9, neuron hidden layer 10, and epoch 2000 produces an accuracy of 60.92%

ANALISIS KUALITAS VOIP YANG BERJALAN DI ATAS PROTOKOL DATAGRAM CONGESTION CONTROL PROTOCOL DAN PENGARUHNYA TERHADAP ALIRAN TCP DENGAN MENGGUNAKAN SIMULATOR NS-2

In this time the Internet is not only used for sending data files or accessing a web site, but have been able to cater for multimedia applications such as video conferencing or Voice over Internet Protocol (VoIP). VoIP technology allows voice communication is done by utilizing the Internet media. VoIP applications generally run on UDP protocol. Increased use of VoIP protocols running on top of protocol that does not implement congestion control mechanism causes the Internet prone to congestion collapse. Datagram Congestion Control Protocol (DCCP) is a transport protocol designed for multimedia Internet applications. This protocol has several congestion control mechanisms and can be selected according to application characteristics. CCID4 is a congestion control mechanism suitable for use with VoIP applications to send a small packet. CCID4 enforces a minimum interval of 10 milliseconds between data packets. This study conducted an analysis of DCCP/CCID4 protocol to determine the quality of VoIP that uses the protocol, comparing the quality produced by the used of UDP protocol, and see the quality of TCP flow as a result of the presence of the VoIP flow on the network. The simulation results show the quality of VoIP tend to do better by using the UDP protocol compared with DCCP/CCID4 when there is no TCP flow on the network. Conversely, when there are TCP flows, the quality of VoIP with DCCP/CCID4 protocol better than UDP. DCCP/CCID4 managed to prevent the congestion collapse compared with the use of UDP. In addition, the TCP quality is better when DCCP/CCID4 protocol used for VoIP applications that experiencing network congestion

Penerapan Metode Fast Independent Component Analysis (FastICA) dalam Memisahkan Vokal dan Instrumen Seni Geguntangan

Abstract. Application of Fast Independent Component Analysis (Fastica) Method in Separating Vocals and Instruments in Geguntangan. Gamelan Geguntangan is often used in religious ceremonies to accompany ceremonies and entertain the public. Along with its development, the Geguntangan gamelan is also used to accompany the Pesantian. Geguntangan recording plays instruments and vocal sounds, most of which have been mixed. The mixed sounds caused the learning process to be less effective for people who will study Pesantian. The students could not focus because of the distracting sound of the instrument. This study aims to separate the sound of instruments and vocals of Geguntangan using deflationary-based FastICA. The non-linear function used is Logcosh. This study also examines the effect of mixing matrix variables and alpha values on nonlinear functions on SDR, SIR, and SAR values. The results of the paired t-test carried out by these two values did not have a significant effect on SDR, SIR, and SAR. The difference in the average time of the mixing matrix testing process is 0.09 seconds and 0.42 seconds for testing the alpha value.Keywords: Pesantian, Geguntangan, BSS, FastICA, Deflationary Based. Abstrak. Gamelan Geguntangan sering dipakai dalam upacara keagamaan baik untuk mengiringi jalannya upacara dan hiburan masyarakat. Seiring perkembangannya, gamelan Geguntangan juga digunakan untuk mengiringi Pesantian. Pada rekaman Geguntangan terdapat suara instrumen dan vokal yang sebagian besarnya sudah tercampur. Hal ini menyebabkan proses belajar yang kurang efektif bagi orang yang akan belajar Pesantian. Para pemelajar tidak bisa fokus karena adanya suara instrumen yang mengganggu. Penelitian ini bertujuan untuk memisahkan suara instrumen dan vokal seni Geguntangan menggunakan deflationary based FastICA. Fungsi non linear yang digunakan adalah Logcosh. Penelitian ini juga menguji pengaruh variabel matriks pencampuran dan nilai alpha pada fungsi nonlinear terhadap nilai SDR, SIR dan SAR. Hasil uji-t berpasangan yang dilakukan kedua nilai ini tidak mempunyai pengaruh yang signifikan terhadap SDR, SIR dan SAR. Selisih rata-rata waktu proses pengujian matriks pencampuran ialah 0.09 detik dan 0.42 detik untuk pengujian nilai alpha.Kata Kunci: Pesantian, Geguntangan, BSS, FastICA, Deflationary Based