feature extraction | DSP & Embedded Electronics

Rangkuti, A.H., Harjoko, A., and Putra, A.E.

This research evolves feature extraction algorithms in overcoming difficulties in classifying batik images that encounter changes in scale and rotation. the algorithm is multiscale and multilevel extended center symmetric local binary pattern (M2ECS-LBP). In utilizing this algorithm using several types of windows to obtain optimal feature extraction results, ranging from the size of 6×6, 9×9, 12 x 12 and 15×15 or a combination of several windows. However, for the use of algorithm carried out sequentially, it also requires a special strategy to obtain optimal image feature extraction results to support performance accuracy in the classification. The results of classification accuracy using K-Nearest neighborhood had reached up until the percentage to 78,4 – 81.7 percent of the image undergoing changes in scale and rotation. However, if the batik image undergoes a change in scale but the rotation is the same then the accuracy percentage can reach 98-99 percent. This algorithm is a very powerful breakthrough with lighter computing techniques than other algorithms. This research can be continued to recognize moving images, expected with maximum accuracy.

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Heriyanto, Hartati and Putra, 2018

The recitation of the Qur’an has its own uniqueness, among others having a special rule in reading and pronunciation, which is called tajwid science. At the time of the Qur’an is recited, there are often mistakes due to the limitations of knowledge of Tajwid. Therefore, the availability of tools to facilitate in checking the appropriateness of recitation is very much needed by those who recite the Qur’an and face limitations in understanding the science of tajwid. Checking the Qur’an reading is a problem that must be solved according to the rules. So far, voice identification studies have problems with feature extraction, compatibility or suitability testing, and accuracy. The issue of feature extraction, suitability, and impermanence testing have been improved in this study, which consists of two stages. The first stage is the extraction of the sound character of the Qur’an reading and the second stage is the testing of the conformity of the Qur’anic recitation and accuracy. In the first stage feature extraction is handled using MFCC and Normalization of Dominant Weight (NDW). Characteristics of reading the Qur’an as reference table is taken from one reader of Al-Qur’an who has competence in the field of science tajwid, for sampling 5-7 people as a source for testing. The process of the second stage of conformity testing of Qur’an reading is done starting from filtering, sequential multiplication of reference table and Conformity Uniformity Pattern (CUP). The sample of reading conformity test is taken from 11 Qur’anic letters containing 8 reading laws and 886 records. The test is performed on the dominant frame, the number of cepstral coefficient and the number of frames. The reading conformance test provides an average accuracy of 91.37% on the nine dominant frames. The test for the number of cepstral coefficients in the c-23 can be an average of 96.65%, while the number of frames on the F-10 is the best average of 96.65%.

[https://doi.org/10.14738/aivp.62.4268]