Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/95169
Title: Music Emotion Classification: Dataset Acquisition and Comparative Analysis
Authors: Panda, Renato 
Paiva, Rui Pedro 
Issue Date: 17-Sep-2012
Project: info:eu-repo/grantAgreement/FCT/5876-PPCDTI/102185/PT/MOODetector - A System for Mood-based Classification and Retrieval of Audio Music 
Serial title, monograph or event: 15th International Conference on Digital Audio Effects – DAFx 2012
Place of publication or event: York, UK
Abstract: In this paper we present an approach to emotion classification in audio music. The process is conducted with a dataset of 903 clips and mood labels, collected from Allmusic database, organized in five clusters similar to the dataset used in the MIREX Mood Classification Task. Three different audio frameworks - Marsyas, MIR Toolbox and Psysound, were used to extract several features. These audio features and annotations are used with supervised learning techniques to train and test various classifiers based on support vector machines. To access the importance of each feature several different combinations of features, obtained with feature selection algorithms or manually selected were tested. The performance of the solution was measured with 20 repetitions of 10-fold cross validation, achieving a F-measure of 47.2% with precision of 46.8% and recall of 47.6%.
URI: http://hdl.handle.net/10316/95169
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Livros de Actas

Show full item record

Page view(s)

35
checked on Jul 23, 2021

Google ScholarTM

Check


This item is licensed under a Creative Commons License Creative Commons