Emotion-based Music Rretrieval on a Well-reduced Audio Feature Space

Maria Magdalena Ruxanda, Bee Yong Chua, Alexandros Nanopoulos, Christian Søndergaard Jensen

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

Resumé

Music expresses emotion. A number of audio extracted features have influence on the perceived emotional expression of music. These audio features generate a high-dimensional space, on which music similarity retrieval can be performed effectively, with respect to human perception of the music-emotion. However, the real-time systems that retrieve music over large music databases, can achieve order of magnitude performance increase, if applying multidimensional indexing over a dimensionally reduced audio feature space. To meet this performance achievement, in this paper, extensive studies are conducted on a number of dimensionality reduction algorithms, including both classic and novel approaches. The paper clearly envisages which dimensionality reduction techniques on the considered audio feature space, can preserve in average the accuracy of the emotion-based music retrieval.
OriginalsprogEngelsk
TidsskriftProceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing
Sider (fra-til)181-184
ISSN1520-6149
DOI
StatusUdgivet - 2009
BegivenhedIEEE International Conference on Acoustics, Speech, and Signal Processing - Taipei, Taiwan
Varighed: 19 apr. 200924 apr. 2009

Konference

KonferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
LandTaiwan
ByTaipei
Periode19/04/200924/04/2009

Fingerprint

Computer music
Real time systems

Bibliografisk note

Volumne: 34

Citer dette

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title = "Emotion-based Music Rretrieval on a Well-reduced Audio Feature Space",
abstract = "Music expresses emotion. A number of audio extracted features have influence on the perceived emotional expression of music. These audio features generate a high-dimensional space, on which music similarity retrieval can be performed effectively, with respect to human perception of the music-emotion. However, the real-time systems that retrieve music over large music databases, can achieve order of magnitude performance increase, if applying multidimensional indexing over a dimensionally reduced audio feature space. To meet this performance achievement, in this paper, extensive studies are conducted on a number of dimensionality reduction algorithms, including both classic and novel approaches. The paper clearly envisages which dimensionality reduction techniques on the considered audio feature space, can preserve in average the accuracy of the emotion-based music retrieval.",
author = "Ruxanda, {Maria Magdalena} and Chua, {Bee Yong} and Alexandros Nanopoulos and Jensen, {Christian S{\o}ndergaard}",
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Emotion-based Music Rretrieval on a Well-reduced Audio Feature Space. / Ruxanda, Maria Magdalena; Chua, Bee Yong; Nanopoulos, Alexandros; Jensen, Christian Søndergaard.

I: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 2009, s. 181-184.

Publikation: Bidrag til tidsskriftKonferenceartikel i tidsskriftForskningpeer review

TY - GEN

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AU - Chua, Bee Yong

AU - Nanopoulos, Alexandros

AU - Jensen, Christian Søndergaard

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PY - 2009

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AB - Music expresses emotion. A number of audio extracted features have influence on the perceived emotional expression of music. These audio features generate a high-dimensional space, on which music similarity retrieval can be performed effectively, with respect to human perception of the music-emotion. However, the real-time systems that retrieve music over large music databases, can achieve order of magnitude performance increase, if applying multidimensional indexing over a dimensionally reduced audio feature space. To meet this performance achievement, in this paper, extensive studies are conducted on a number of dimensionality reduction algorithms, including both classic and novel approaches. The paper clearly envisages which dimensionality reduction techniques on the considered audio feature space, can preserve in average the accuracy of the emotion-based music retrieval.

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