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Title: Music Emotion Recognition: The Importance of Melodic Features
Authors: Rocha, Bruno 
Panda, Renato 
Paiva, Rui Pedro 
Keywords: audio; machine learning; melodic features; music emotion recognition
Issue Date: 2013
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: 6th International Workshop on Music and Machine Learning – MML 2013 – in conjunction with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases – ECML/PKDD 2013
Place of publication or event: Prague, Czech Republic
Abstract: We study the importance of a melodic audio (MA) feature set in music emotion recognition (MER) and compare its performance to an approach using only standard audio (SA) features. We also analyse the fusion of both types of features. Employing only SA features, the best attained performance was 46.3%, while using only MA features the best outcome was 59.1% (F- measure). A combination of SA and MA features improved results to 64%. These results might have an important impact to help break the so-called glass ceiling in MER, as most current approaches are based on SA features.
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Livros de Actas

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