Saron Music Transcription Based on Rhythmic Information Using HMM on Gamelan Orchestra

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

Saron Music Transcription Based on Rhythmic Information using HMM on Gamelan Orchestra

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.

Impulsive spike enhancement on gamelan audio using harmonic percussive separation

Impulsive spikes often occur in audio recording of gamelan where most existing methods reduce it. This research offers new method to enhance audio impulsive spike in gamelan music that is able to reduce, eliminate and even strengthen spikes. The process separates audio components into harmonics and percussive components. Percussion component is set to rise or lowered, and the results of the process combined with harmonic components again. This study proposes a new method that allows reducing, eliminating and even amplifying the spike. From the similarity test using the Cosine Distance method, it is seen that spike enhancement through Harmonic Percussive Source Separation (HPSS) has an average Cosine Distance value of 0.0004 or similar to its original, while Mean Square Error (MSE) has an average value of 0.0004 that is very small in average error and also very similar. From the Perceptual Evaluation of Audio Quality (PEAQ) testing with Harmonic Percussive Source Separation (HPSS), it has a better quality with an average Objective Difference Grade (ODG) of -0.24 or Imperceptible

Segmentation of Identical and Simultaneously Played Traditional Music Instruments using Adaptive

Nowadays, mining of the musical ensemble has become very crucial since the information inside a musical ensemble is required by any musical contents services. In this research, we introduce Gamelan as one of the Indonesian traditional music instruments as our research objective. To indicate the changes of Gamelan features (i.e. tempo also the hammer struck styles) the segmentation of Gamelan music instruments is required as the music tagging tools. Adaptive LMS is employed for segmenting identical instruments that are played in the concurrent fashion. The target is to find how many instruments are played at the same time or separated by very short time (≤ 1 ms). The experiment results demonstrate robust detection with 0.02 ms accuracy for segmenting identical and simultaneously played Gamelan instruments. These results are employed for indicating the changes of Gamelan features, such as tempo also the hammer struck styles

Gamelan Music Onset Detection based on Spectral Features

This research detects onsets of percussive instruments by examining the performance on the sound signals of gamelan instruments as one of  traditional music instruments in Indonesia. Onset plays important role in determining musical rythmic structure, like beat, tempo, measure, and is highly required in many applications of music information retrieval. Four onset detection methods that employ spectral features, such as magnitude, phase, and the combination of both are compared in this paper. They are phase slope (PS), weighted phase deviation (WPD), spectral flux (SF), and rectified complex domain (RCD). Features are extracted by representing the sound signals into time-frequency domain using overlapped Short-time Fourier Transform (STFT) and by varying the window length. Onset detection functions are processed through peak-picking using dynamic threshold. The results showed that by using suitable window length and parameter setting of dynamic threshold, F-measure which is greater than 0.80 can be obtained for certain methods

Spectral-based Features Ranking for Gamelan Instruments Identification using Filter Techniques

 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%

Color Clustering in the Metal Inscription Images Using ANFIS Filter

Ancient inscriptions are historical records of the past age made on stone or metal media. Currently many ancient inscriptions were damaged because it is too long buried in the ground. This research is the first step to repairing the damaged inscription using Image processing. Efforts to restorations using color clustering with ANFIS method are an early stage to perform letters segmentation in the ancient inscription. The Results of ANFIS clustering method are compared to the spatial fuzzy clustering method (SFCM). The clustering performance measurement is done by measuring root mean square error (RMSE). From RMSE measurements, the average values obtained with ANFIS clustering method is smaller 21.80% than with SFCM. This means there is an increase in clustering performance with ANFIS method compared to SFCM.

Combination of Cluster Method for Segmentation of Web Visitors

Clustering is one of the important part in web usage miningfor the purpose of segmenting visitors. This action is very important for web personalization orweb modification. In this paper, we perform clustering of the web visitors using a combination of methods of hierarchical and non-hierarchical clustering toward web log data. Hierarchical clustering method used to determine the number of clusters, and non-hierarchical clustering method is used in forming clusters. The stages of cluster analysis are preceded by pre-processing the data and factor analysis. With this approach, the owner of the web is more effective at finding access patterns of web visitors and can have new knowledge about visitors’ segmentation. From the test applied on ITS’s web log data, 6 clusters of web visitors are resulted. Among the 6 cluster, cluster 3 has the biggest number of members. This information can be useful for web management to pay attention on members’ behavioral patterns of the 3rd cluster’s either to make personalization or modification on the web. The test results show the feasibility and efficiency of application of this method

Clustering Tingkat Risiko Klasifikasi Lapangan Usaha (KLU) Menggunakan Metode K-Means

Pajak adalah sumber utama pendapatan negara. Karena itu, otoritas pajak di seluruh dunia, bertugas untuk mengurangi kesenjangan pajak (tax gap). Salah satu faktor yang menyebabkan adanya kesenjangan pajak adalah tingkat kepatuhan Wajib Pajak (WP). Dalam upaya meminimalisir ketidakpatuhan WP, Direktorat Jenderal Pajak (DJP) melakukan kegiatan pengawasan dan pemeriksaan terhadap WP. WP badan mempunyai kontribusi dominan terhadap penerimaan negara. Dari latar belakang tersebut, diambil pendekatan menggunakan metode clustering dengan algoritma K-Means untuk mengelompokkan Klasifikasi Lapangan Usaha (KLU) sesuai risiko tingkat ketidakpatuhan dan dampak fiskal bagi penerimaan yang dibagi menjadi tinggi, sedang, dan rendah. Hasil pengujian menunjukkan terdapat 9 KLU yang masuk ke dalam kuadran berwarna merah dengan tingkat ketidakpatuhan tinggi (variabel A) dan memiliki dampak fiskal yang tinggi (variabel B). Dengan validasi clustering menggunakan uji silhouette, diperoleh nilai 0,65 untuk variabel A dan 0,93 untuk variabel B. Informasi yang dihasilkan dari penelitian ini dapat dipergunakan untuk mendukung pengambilan keputusan dalam penentuan daftar KLU yang perlu diprioritaskan untuk dilakukan pemeriksaan dan pengawasan. AbstractTax are the main source of state revenue. Therefore, tax authorities around the world are in charged to reducing the tax gap. One of the factors that causes the tax gap is the level of taxpayer compliance. In order to minimising the risk of taxpayers non-compliance, Directorate General of Taxes (DGT) as a Tax Authorities need to supervising and inspecting taxpayers. Corporate taxpayers as one of the largest source of revenue, based on their business sector have a dominant contribution to state revenues. Thus, in this study, the researcher tries to implement the clustering method with the K-Means algorithm to grouping the business classifications (Klasifikasi Lapangan Usaha/KLU) according to the risk of non-compliance level and the fiscal impact on revenue which is divided into high, medium, and low. The results show that there are 9 KLUs that included into the red quadrant with a high level of non-compliance (variable A) and have a high fiscal impact (variable B). Clustering validation using the silhouette test, obtained values of variable A and variable B, respectively 0,65 and 0,93. The information provided from this study can be used to support decision making in determining the list of KLUs that need to be prioritized for supervising and inspecting