Classification of educational backgrounds of students using musical intelligence and perception with the help of genetic neural networks

Authors:
Fırat Hardalaç
Affiliations:
Department of Computer Engineering, Faculty of Engineering, Kirikkale University, Kirikkale, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

Citing 2
Cited 1

Genetic Algorithms in Search, Optimization and Machine Learning

Genetic Algorithms in Search, Optimization and Machine Learning
Principles of Neurocomputing for Science and Engineering

Principles of Neurocomputing for Science and Engineering

The statistical measurements and neural network analysis of the effect of musical education to musical hearing and sensing

Expert Systems with Applications: An International Journal

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Abstract

In this study, we demonstrate that machine learning can be used to classify students who had backgrounds in positive-sciences (including engineering, science and math disciplines) vs. social-sciences (including arts and humanities disciplines) by the help of musical hearing and perception using genetic neural networks. Our 80 test subjects had an even mixture of both aforementioned disciplines. Each participant is asked to listen to a melody played on a piano and to repeat the melody himself verbally. Both the original melody and participants repetition is recorded and frequency and amplitude response is analyzed by using fast Fourier transform (FFT). This information is applied to hybrid genetic algorithm and neural networks as learning data and the training of the feed forward neural network is realized. Our results show that by using musical perception our genetic neural network classifies students with positive- and social-science backgrounds at a success rate of 95% and 90%, respectively.