Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/95164
Title: | Dimensional Music Emotion Recognition: Combining Standard and Melodic Audio Features | Authors: | Panda, Renato Rocha, Bruno Paiva, Rui Pedro |
Keywords: | music emotion recognition; machine learning; regression; standard audio features; melodic features | Issue Date: | 15-Oct-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: | 10th International Symposium on Computer Music Multidisciplinary Research – CMMR 2013 | Place of publication or event: | Marseille, France | Abstract: | We propose an approach to the dimensional music emotion recognition (MER) problem, combining both standard and melodic audio features. The dataset proposed by Yang is used, which consists of 189 audio clips. From the audio data, 458 standard features and 98 melodic features were extracted. We experimented with several supervised learning and feature selection strategies to evaluate the proposed approach. Employing only standard audio features, the best attained performance was 63.2% and 35.2% for arousal and valence prediction, respectively (R2 statistics). Combining standard audio with melodic features, results improved to 67.4 and 40.6%, for arousal and valence, respectively. To the best of our knowledge, these are the best results attained so far with this dataset. | URI: | https://hdl.handle.net/10316/95164 | Rights: | openAccess |
Appears in Collections: | I&D CISUC - Artigos em Livros de Actas |
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File | Description | Size | Format | |
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Panda, Rocha, Paiva - 2013 - Dimensional Music Emotion Recognition Combining Standard and Melodic Audio Features.pdf | 285.74 kB | Adobe PDF | View/Open |
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