Akademik Veri Yönetim Sistemi
EXPERT SYSTEMS WITH APPLICATIONS, cilt.38, sa.8, ss.9517-9521, 2011 (SCI-Expanded)
This work presents the help of music education to musical hearing, the sensing of hearing at the end of education, and the affection of hearing levels of young people. In this study, neural network is used for classification of students using musical hearing and sensing. We demonstrate that machine learning can be used to predict the students musical perception, who entered to the Education Faculty, using neural networks. The pure tone audiometric measurements were realized for the evaluation of hearing at the frequencies 250, 500, 1000, 2000, and 8000 Hz. The evaluation of musical hearing for the students was achieved as: single tone-vertical hearing, poly tone-horizontal hearing and melody and rhythm hearing. The testing of musical hearing and sensing of students were compared with the test after two-year education. It was observed that the tests after two-year education offered good performances at all frequency level and this is meaningful in statistically, While musical hearing sensitivity is significantly high in horizontal and rhythm hearing tests, it is not changed in vertical hearing tests. Our results show that by using musical hearing and sensing our neural network classifies students whether they are at musical Education Department or other educational department of Education Faculty at a success rate of 92% and 88%, respectively. (C) 2011 Elsevier Ltd. All rights reserved.