Parametrisation and correlation analysis applied to music mood classification

Authors:
Bożena Kostek;Magdalena Plewa
Affiliations:
Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland;Gdansk University of Technology, Narutowicza 11/12, 80-233 Gdansk, Poland
Venue:
International Journal of Computational Intelligence Studies
Year:
2013

Citing 8
Cited 0

MUSIPER: a system for modeling music similarity perception based on objective feature subset selection

User Modeling and User-Adapted Interaction
Clustering

Clustering
Exploiting genre for music emotion classification

ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
Visual affect recognition

Visual affect recognition
Music Emotion Recognition

Music Emotion Recognition
Report of the ISMIS 2011 contest: music information retrieval

ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Towards extracting emotions from music

IMTCI'04 Proceedings of the Second international conference on Intelligent Media Technology for Communicative Intelligence
Automatic mood detection and tracking of music audio signals

IEEE Transactions on Audio, Speech, and Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The paper presents a study on music mood categorisation. First, a review of music mood models is presented. Then, the preparation of a set of music excerpts to be used in the experiments and music parametrisation is described. Next, some listening tasks performed to obtain mood descriptors are introduced. Finally, the correlation between mood descriptors and features extracted from parameters is discussed. The paper concludes with some observations derived from the analysis performed.