Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/94384
Title: Music Emotion Recognition with Standard and Melodic Audio Features
Authors: Panda, Renato 
Rocha, Bruno
Paiva, Rui Pedro 
Keywords: Music emotion recognition; Melody; Melodic audio features
Issue Date: 2015
Publisher: Taylor & Francis
Project: RECARDI (QREN 22997) 
info:eu-repo/grantAgreement/FCT/5876-PPCDTI/102185/PT/MOODetector - A System for Mood-based Classification and Retrieval of Audio Music 
info:eu-repo/grantAgreement/FCT/SFRH/SFRH/BD/91523/2012/PT/EMOTION-BASED ANALYSIS AND CLASSIFICATION OF AUDIO MUSIC 
Serial title, monograph or event: Applied Artificial Intelligence (AAI)
Volume: 29
Issue: 4
Abstract: We propose a novel approach to music emotion recognition by combining standard and melodic features extracted directly from audio. To this end, a new audio dataset organized similarly to the one used in MIREX mood task comparison was created. From the data, 253 standard and 98 melodic features are extracted and used with several supervised learning techniques. Results show that, generally, melodic features perform better than standard audio. The best result, 64% f-measure, with only 11 features (9 melodic and 2 standard), was obtained with ReliefF feature selection and Support Vector Machines.
URI: http://hdl.handle.net/10316/94384
ISSN: 0883-9514
1087-6545
DOI: 10.1080/08839514.2015.1016389
Rights: embargoedAccess
Appears in Collections:I&D CISUC - Artigos em Revistas Internacionais

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