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https://hdl.handle.net/10316/95170
Título: | Automatic Creation of Mood Playlists in the Thayer Plane: A Methodology and a Comparative Study | Autor: | Panda, Renato Paiva, Rui Pedro |
Palavras-chave: | classification; mood detection; music emotion recognition; playlist generation; regression | Data: | 6-Jul-2011 | Projeto: | info:eu-repo/grantAgreement/FCT/5876-PPCDTI/102185/PT/MOODetector - A System for Mood-based Classification and Retrieval of Audio Music | Título da revista, periódico, livro ou evento: | 8th Sound and Music Computing Conference (SMC2011) | Local de edição ou do evento: | Padova, Italy | Resumo: | 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. | URI: | https://hdl.handle.net/10316/95170 | ISBN: | 978-88-97385-03-5 | ISSN: | 2518-3672 | DOI: | 10.5281/zenodo.849887 10.5281/zenodo.849886 |
Direitos: | openAccess |
Aparece nas coleções: | I&D CISUC - Artigos em Livros de Actas |
Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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Panda, Paiva - 2011 - Automatic Creation of Mood Playlists in the Thayer Plane A Methodology and a Comparative Study.pdf | 515.8 kB | Adobe PDF | Ver/Abrir |
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