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Serveur d'exploration sur la musique en Sarre

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Unsupervised Music Structure Annotation by Time Series Structure Features and Segment Similarity : Music Data Mining

Identifieur interne : 000002 ( PascalFrancis/Corpus ); précédent : 000001; suivant : 000003

Unsupervised Music Structure Annotation by Time Series Structure Features and Segment Similarity : Music Data Mining

Auteurs : Joan Serra ; Meinard Müller ; Peter Grosche ; Josep Ll. Arcos

Source :

RBID : Pascal:14-0220601

Descripteurs français

English descriptors

Abstract

Automatically inferring the structural properties of raw multimedia documents is essential in today's digitized society. Given its hierarchical and multi-faceted organization, musical pieces represent a challenge for current computational systems. In this article, we present a novel approach to music structure annotation based on the combination of structure features with time series similarity. Structure features encapsulate both local and global properties of a time series, and allow us to detect boundaries between homogeneous, novel, or repeated segments. Time series similarity is used to identify equivalent segments, corresponding to musically meaningful parts. Extensive tests with a total of five benchmark music collections and seven different human annotations show that the proposed approach is robust to different ground truth choices and parameter settings. Moreover, we see that it outperforms previous approaches evaluated under the same framework.

Notice en format standard (ISO 2709)

Pour connaître la documentation sur le format Inist Standard.

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A11 01  1    @1 SERRA (Joan)
A11 02  1    @1 MÜLLER (Meinard)
A11 03  1    @1 GROSCHE (Peter)
A11 04  1    @1 ARCOS (Josep Ll.)
A14 01      @1 IIIA-CSIC, Campus de la UAB s/n @2 08193 Bellaterra @3 ESP @Z 1 aut. @Z 4 aut.
A14 02      @1 International Audio Laboratories Erlangen @2 91058 Erlangen @3 DEU @Z 2 aut.
A14 03      @1 Saarland University and the Max-Planck Institut für Informatik, Campus E1.4 @2 66123 Saarbrücken @3 DEU @Z 3 aut.
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Format Inist (serveur)

NO : PASCAL 14-0220601 INIST
ET : Unsupervised Music Structure Annotation by Time Series Structure Features and Segment Similarity : Music Data Mining
AU : SERRA (Joan); MÜLLER (Meinard); GROSCHE (Peter); ARCOS (Josep Ll.)
AF : IIIA-CSIC, Campus de la UAB s/n/08193 Bellaterra/Espagne (1 aut., 4 aut.); International Audio Laboratories Erlangen/91058 Erlangen/Allemagne (2 aut.); Saarland University and the Max-Planck Institut für Informatik, Campus E1.4/66123 Saarbrücken/Allemagne (3 aut.)
DT : Publication en série; Niveau analytique
SO : IEEE transactions on multimedia; ISSN 1520-9210; Etats-Unis; Da. 2014; Vol. 16; No. 5; Pp. 1229-1240; Bibl. 44 ref.
LA : Anglais
EA : Automatically inferring the structural properties of raw multimedia documents is essential in today's digitized society. Given its hierarchical and multi-faceted organization, musical pieces represent a challenge for current computational systems. In this article, we present a novel approach to music structure annotation based on the combination of structure features with time series similarity. Structure features encapsulate both local and global properties of a time series, and allow us to detect boundaries between homogeneous, novel, or repeated segments. Time series similarity is used to identify equivalent segments, corresponding to musically meaningful parts. Extensive tests with a total of five benchmark music collections and seven different human annotations show that the proposed approach is robust to different ground truth choices and parameter settings. Moreover, we see that it outperforms previous approaches evaluated under the same framework.
CC : 001D02C03; 001B40C75; 001D02B07D; 001B40C38
FD : Multimédia; Recherche information; Similitude; Annotation; Présentation document; Délai d'exécution; Réalité terrain; Acoustique musicale; Série temporelle; Solution similitude; Système hiérarchisé; Méthode globale locale; Musique; Etude expérimentale; Acoustique audio; Apprentissage non supervisé; Recherche par contenu
ED : Multimedia; Information retrieval; Similarity; Annotation; Document layout; Time allowed; Ground truth; Musical acoustics; Time series; Similarity solution; Hierarchical system; Global local method; Music; Experimental study; Audio acoustics; Unsupervised learning; Content-based retrieval
SD : Multimedia; Búsqueda información; Similitud; Anotación; Presentación documento; Plazo ejecución; Realidad terreno; Acústica musical; Serie temporal; Solución semejanza; Sistema jerarquizado; Método global local; Música; Estudio experimental; Acústica audio; Aprendizaje no supervisado; Búsqueda por Contenidos
LO : INIST-26826.354000504838830050
ID : 14-0220601

Links to Exploration step

Pascal:14-0220601

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<s5>23</s5>
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<s5>25</s5>
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<s5>25</s5>
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<s5>26</s5>
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<s5>26</s5>
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<s5>33</s5>
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<s5>33</s5>
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<s5>96</s5>
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<s4>CD</s4>
<s5>96</s5>
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<s5>96</s5>
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<s4>CD</s4>
<s5>97</s5>
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<s0>Unsupervised learning</s0>
<s4>CD</s4>
<s5>97</s5>
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<s0>Aprendizaje no supervisado</s0>
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<s5>97</s5>
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<s5>98</s5>
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<s0>Content-based retrieval</s0>
<s4>CD</s4>
<s5>98</s5>
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<fC03 i1="17" i2="X" l="SPA">
<s0>Búsqueda por Contenidos</s0>
<s4>CD</s4>
<s5>98</s5>
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<s1>265</s1>
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<NO>PASCAL 14-0220601 INIST</NO>
<ET>Unsupervised Music Structure Annotation by Time Series Structure Features and Segment Similarity : Music Data Mining</ET>
<AU>SERRA (Joan); MÜLLER (Meinard); GROSCHE (Peter); ARCOS (Josep Ll.)</AU>
<AF>IIIA-CSIC, Campus de la UAB s/n/08193 Bellaterra/Espagne (1 aut., 4 aut.); International Audio Laboratories Erlangen/91058 Erlangen/Allemagne (2 aut.); Saarland University and the Max-Planck Institut für Informatik, Campus E1.4/66123 Saarbrücken/Allemagne (3 aut.)</AF>
<DT>Publication en série; Niveau analytique</DT>
<SO>IEEE transactions on multimedia; ISSN 1520-9210; Etats-Unis; Da. 2014; Vol. 16; No. 5; Pp. 1229-1240; Bibl. 44 ref.</SO>
<LA>Anglais</LA>
<EA>Automatically inferring the structural properties of raw multimedia documents is essential in today's digitized society. Given its hierarchical and multi-faceted organization, musical pieces represent a challenge for current computational systems. In this article, we present a novel approach to music structure annotation based on the combination of structure features with time series similarity. Structure features encapsulate both local and global properties of a time series, and allow us to detect boundaries between homogeneous, novel, or repeated segments. Time series similarity is used to identify equivalent segments, corresponding to musically meaningful parts. Extensive tests with a total of five benchmark music collections and seven different human annotations show that the proposed approach is robust to different ground truth choices and parameter settings. Moreover, we see that it outperforms previous approaches evaluated under the same framework.</EA>
<CC>001D02C03; 001B40C75; 001D02B07D; 001B40C38</CC>
<FD>Multimédia; Recherche information; Similitude; Annotation; Présentation document; Délai d'exécution; Réalité terrain; Acoustique musicale; Série temporelle; Solution similitude; Système hiérarchisé; Méthode globale locale; Musique; Etude expérimentale; Acoustique audio; Apprentissage non supervisé; Recherche par contenu</FD>
<ED>Multimedia; Information retrieval; Similarity; Annotation; Document layout; Time allowed; Ground truth; Musical acoustics; Time series; Similarity solution; Hierarchical system; Global local method; Music; Experimental study; Audio acoustics; Unsupervised learning; Content-based retrieval</ED>
<SD>Multimedia; Búsqueda información; Similitud; Anotación; Presentación documento; Plazo ejecución; Realidad terreno; Acústica musical; Serie temporal; Solución semejanza; Sistema jerarquizado; Método global local; Música; Estudio experimental; Acústica audio; Aprendizaje no supervisado; Búsqueda por Contenidos</SD>
<LO>INIST-26826.354000504838830050</LO>
<ID>14-0220601</ID>
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