Please use this identifier to cite or link to this item:
Title: Automatic Creation of Mood Playlists in the Thayer Plane: A Methodology and a Comparative Study
Authors: Panda, Renato 
Paiva, Rui Pedro 
Keywords: classification; mood detection; music emotion recognition; playlist generation; regression
Issue Date: 6-Jul-2011
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: 8th Sound and Music Computing Conference (SMC2011)
Place of publication or event: Padova, Italy
Abstract: We propose an approach for the automatic creation of mood playlists in the Thayer plane (TP). Music emotion recognition is tackled as a regression and classification problem, aiming to predict the arousal and valence (AV) values of each song in the TP, based on Yang's dataset. To this end, a high number of audio features are extracted using three frameworks: PsySound, MIR Toolbox and Marsyas. The extracted features and Yang's annotated AV values are used to train several Support Vector Regressors, each employing different feature sets. The best performance, in terms of R2statistics, was attained after feature selection, reaching 63% for arousal and 35.6% for valence. Based on the predicted location of each song in the TP, mood playlists can be created by specifying a point in the plane, from which the closest songs are retrieved. Using one seed song, the accuracy of the created playlists was 62.3% for 20-song playlists, 24.8% for 5-song playlists and 6.2% for the top song.
ISBN: 978-88-97385-03-5
ISSN: 2518-3672
DOI: 10.5281/zenodo.849887
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Livros de Actas

Show full item record

Page view(s)

checked on Jul 29, 2021


checked on Jul 29, 2021

Google ScholarTM




This item is licensed under a Creative Commons License Creative Commons