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A survey on automatic music emotion recognition system and some analysis of the bottleneck
PhD Qualifying Examination Title: "A survey on automatic music emotion recognition system and some analysis of the bottleneck" by Mr. Yu HONG Abstract: Automatic music emotion recognition (MER) system is quite useful in music retrieval and recommendation, and previous research has made great progress on it. In this paper, we will give an overview of the emotion representation, general framework of the MER system, current achievement, and the bottlenecks. Previous research has shown that both listeners and automated systems often have difficulty distinguishing low-arousal categories such as Calm and Sad. This paper also 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, and covered about 92% of the low-arousal space, where 8% of the space were holes that were notat- all Calm or Sad. 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. Date: Thursday, 8 December 2016 Time: 3:00pm - 5:00pm Venue: Room 2129C Lift 19 Committee Members: Prof. Andrew Horner (Supervisor) Prof. Huamin Qu (Chairperson) Dr. Pan Hui Dr. Xiaojuan Ma **** ALL are Welcome ****