Speaker Recognition For Digital Forensic Audio Analysis Using Learning Vector Quantization Method

Author(s):  
Danny Bastian Manurung ◽  
Burhanuddin Dirgantoro ◽  
Casi Setianingsih
2015 ◽  
Vol 3 (1) ◽  
pp. 26 ◽  
Author(s):  
Endah Purwanti ◽  
Prihartini Widiyanti

In this paper, we are developing an automated method for the detection of tubercle bacilli in clinical specimens, principally the sputum. This investigation is the first attempt to automatically identify TB bacilli in sputum using image processing and learning vector quantization (LVQ) techniques. The evaluation of the learning vector quantization (LVQ) was carried out on Tuberculosis dataset show that average of accuracy is 91,33%.


Author(s):  
Eko Arianto ◽  
Laifa Rahmawati

One of the lessons for mental disorder students in Special Schools is practicum lessons in the form of vocational education. This lesson uses equipment that requires prudence. Mental disorder students have characteristics that are low memory and move based on intuition. Teachers should pay extra attention especially to detect student behavior during the learning. This detection is needed for learning to take place smoothly and students are safe from the dangers around the practicum place. Teacher's feedback on the detection obtained in the form of a warning from the teacher. This study is expected to be useful for providing a special detection pattern for students to assist teachers by providing feedback in the form of warnings using natural motion detection technology. This research was conducted using Kinect as data input and data was processed using artificial neural network and Learning Vector Quantization method. The dangerous attitude used in the test is the attitude of standing at the time of drilling position. The data used by training is 126 data and do training using LVQ. At the LVQ training stage, the training was conducted with parameter of Learning Rate 0,05, maximum Iteration 44, reduction of learning rate 0.01, and Learning rate minimum 0,02.


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