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


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