K-Means kümeleme algoritması verileri K giriş parametre sayısı kadar kümeye bölmektedir. Bu çalışmanın amacı ise kümelemeyi otomatik hale getirmek ve dışarıdan K parametresinin girilmesine gerek kalınmadan verileri uygun küme sayısınca kümelere yerleştirmektir. Geliştirilen otomatik K-Means algoritması sayısal veriler ve görüntüler üzerinde test edilmiş ve başarılı sonuçlara ulaşılmıştır.

The aim of this study is to bring a new perspective for the classification of the songs, revealing of the colors of music. The first effort is to transform songs into images. The colorful images have been attained with Short time Fourier transform, discrete cosine transform and time to spatial transformation and some extra processing. It has been observed that the images of different music genres obtained with the same method have different colors. But some of them have similar colors and patterns, which making difficult to classify. Pre-trained deep convolutional network have been trained with these images. For five Turkish musical genres, nearly up to 60% classification accuracy has been achieved and for ten musical genre of a benchmark musical dataset nearly up to 54% classification accuracy has been achieved. In future studies, it has been planned to create the images using timbral texture and rhythmic contents, for increasing the accuracy.