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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 ****