Abstrakt
Publicly available multimedia systems provide users with plenty of music files offered in different genres. These systems should process the music as fast as possible while satisfying the needs of their users as well.
In this context, reliable music classification represents one of the major challenges. Classification systems without deeper knowledge of music structure and composition yield to considerable errors.
In some cases, music can not be classified clearly due to an overlap in genres. However, in other cases, we can clarify the classification simply by using the approach of a skilled musician.
In this paper, we develop a new approach to automatic music classification inspired by the theory of neural networks, enhanced by deeper knowledge of tonal harmony. Based on a new measure derived from harmonic movements, harmonic complexity, our supporting experiments proved a significant improvement in classification accuracy.