An analysis of low-arousal piano music ratings to uncover what makes calm and sad music so difficult to distinguish in music emotion recognition

Abstract

Music emotion recognition and recommendation systems often use a simplified 4-quadrant model with categories such as Happy, Sad, Angry, and Calm. Previous research has shown that both listeners and automated systems often have difficulty distinguishing low-arousal categories such as Calm and Sad. This paper seeks to explore what makes the categories Calm and Sad so difficult to distinguish. We used 300 low-arousal excerpts from the classical piano repertoire to determine the coverage of the categories Calm and Sad in the low-arousal space, their overlap, and their balance to one another. Our results show that Calm was 40% bigger in terms of coverage than Sad, but that on average Sad excerpts were significantly more negative in mood than Calm excerpts were positive. Calm and Sad overlapped in nearly 20% of the excerpts, meaning 20% of the excerpts were about equally Calm and Sad. Calm and Sad covered about 92% of the low-arousal space, where 8% of the space were holes that were not-at-all Calm or Sad. The largest holes were for excerpts considered Mysterious and Doubtful, but there were smaller holes among positive excerpts as well. Due to the holes in the coverage, the overlaps, and imbalances the Calm-Sad model adds about 6% more errors when compared to asking users directly whether the mood of the music is positive or negative. Nevertheless, the Calm-Sad model is still useful and appropriate for applications in music emotion recognition and recommendation such as when a simple and intuitive interface is preferred or when categorization is more important than precise differentiation.