MOODetector: A System for Mood-based Classification and Retrieval of Audio Music

Description

This project addresses the topic of Music Emotion Recognition. Research topics: feature extraction, selection and evaluation; extraction of knowledge from computational models; tracking of mood variations. “Music’s preeminent functions are social and psychological”, and so “the most useful retrieval indexes are those that facilitate searching in conformity with such social and psychological functions. Typically, such indexes will focus on stylistic, mood, and similarity information” (David Huron, 2000). This is supported by studies on music information behaviour that have identified music mood as an important criterion for music retrieval and organization. Besides the music industry, the range of applications of mood detection in music is wide and varied, e.g., game development, cinema, advertising or the clinical area (in the motivation to compliance to sport activities prescribed by physicians, as well as stress management). Compared to music emotion synthesis, few works have been devoted to emotion analysis. From these, most of them deal with MIDI or symbolic representations. Only a few works tackle the problem of emotion detection in audio music signals, the first one we are aware of published in 2003. Being a very recent research topic, many limitations can be found and several problems are still open. In fact, the present accuracy of those systems shows there is plenty of room for improvement. In a recent comparison, the best algorithm achieved 65% classification accuracy in a task comprising 5 categories (MIREX 2010). The effectiveness of such systems demands research on feature extraction, selection and evaluation, extraction of knowledge from computational models and the tracking of mood variations throughout a song. These are the main goals of this project.

Researchers

Funded by

FCT: PTDC/EIA-EIA/102185/2008

Partners

None

Total budget

77 304,00 €

Local budget

77 304,00 €

Keywords

Music Emotion Recognition, Music Information Retrieval

Start Date

2010-05-16

End Date

2013-11-15

Journal Articles

Conference Articles