Classification of educational backgrounds of students using musical intelligence and perception wilh the help of artificial neural networks - Hardalac, Naciye; Ercan, Nevhiz; Hardalac, Firat; Ergut, Salih (Ieee, 2006)
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 artificial 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 then used to train a neural network. Our results show that by using musical perception our neural network classifies students with positive and social science backgrounds at a success rate of 90% and 85%, respectively.
36th Annual Frontiers in Education (FIE 2006) -- OCT 28-31, 2006 -- San Diego, CA
Keywords: artificial neural networks (ANN); fast fourier transform (FFT); education; musical hearing; pure tone audiometry