Music sparse decomposition onto a MIDI dictionary of musical words and its application to music mood classification

Boyang Gao 1 Emmanuel Dellandréa 1 Liming Chen 1
1 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Most of the automated music analysis methods available in the literature rely on the representation of the music through a set of low-level audio features related to temporal and frequential properties. Identifying high-level concepts, such as music mood, from this "black-box" representation is particularly challenging. Therefore we present in this paper a novel music representation that allows gaining an in-depth understanding of the music structure. Its principle is to decompose sparsely the music into a basis of elementary audio elements, called musical words, which represent the notes played by various instruments generated through a MIDI synthesizer. From this representation, a music feature is also proposed to allow automatic music classification. Experiments driven on two music datasets have shown the effectiveness of this approach to represent accurately music signals and to allow efficient classification for the complex problem of music mood classification.
Type de document :
Communication dans un congrès
International Workshop on Content-Based Multimedia Indexing (CBMI) , Jun 2012, Annecy, France. IEEE, pp.1-6, 2012, <10.1109/CBMI.2012.6269798>
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https://hal.archives-ouvertes.fr/hal-01353057
Contributeur : Équipe Gestionnaire Des Publications Si Liris <>
Soumis le : mercredi 10 août 2016 - 16:20:47
Dernière modification le : jeudi 11 août 2016 - 01:04:18

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Boyang Gao, Emmanuel Dellandréa, Liming Chen. Music sparse decomposition onto a MIDI dictionary of musical words and its application to music mood classification. International Workshop on Content-Based Multimedia Indexing (CBMI) , Jun 2012, Annecy, France. IEEE, pp.1-6, 2012, <10.1109/CBMI.2012.6269798>. <hal-01353057>

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