Rough sets applied to mood of music recognition

Mood of music is considered as one of the most intuitive criteria for listeners, thus this work is focused on the emotional content of music and its automatic recognition. The research study presented in this work contains an attempt to music emotion recognition including audio parameterization and rough sets. A music set consisting of 154 excerpts from 10 music genres was evaluated in the listening experiment. This may be treated as a ground truth. The results achieved indicated a strong correlation between subjective results and objective descriptors and on that basis a vector of parameters related to mood of music was created. On the other hand, rough set-based processing was applied to derive reducts containing the most promising features in the context of mood recognition, as well as confusion matrices of the mood recognition. Both approaches indicate strong relationship between objective descriptors and subjective evaluation of mood of music.