Classification of educational backgrounds of students using musical intelligence and perception with the help of artificial neural networks
36th ASEE/IEEE Frontiers in Education Conference, FIE, San Diego, CA, Amerika Birleşik Devletleri, 28 - 31 Ekim 2006, ss.9-14, (Tam Metin Bildiri)
- Yayın Türü: Bildiri / Tam Metin Bildiri
- Doi Numarası: 10.1109/fie.2006.322628
- Basıldığı Şehir: San Diego, CA
- Basıldığı Ülke: Amerika Birleşik Devletleri
- Sayfa Sayıları: ss.9-14
- Anahtar Kelimeler: Artificial Neural Networks (ANN), Education, Fast Fourier Transform (FFT), Musical hearing, Pure tone audiometry
- Gazi Üniversitesi Adresli: Evet
Özet
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. © 2006 IEEE.