MusicSarreV3, PascalFrancis, Checkpoint, bibRecord, 000000

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

Identifieur interne : 000000 ( PascalFrancis/Checkpoint ); suivant : 000001

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

Auteurs : Joan Serra [Espagne] ; Meinard Müller [Allemagne] ; Peter Grosche [Allemagne] ; Josep Ll. Arcos [Espagne]

Source :

IEEE transactions on multimedia [ 1520-9210 ] ; 2014.

RBID : Pascal:14-0220601

Descripteurs français

Pascal (Inist)
- 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.
Wicri :
- topic : Multimédia, Musique.

English descriptors

KwdEn :
- Annotation, Audio acoustics, Content-based retrieval, Document layout, Experimental study, Global local method, Ground truth, Hierarchical system, Information retrieval, Multimedia, Music, Musical acoustics, Similarity, Similarity solution, Time allowed, Time series, Unsupervised learning.

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.

Affiliations:

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Pascal:14-0220601

Le document en format XML

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   |texte=   Unsupervised Music Structure Annotation by Time Series Structure Features and Segment Similarity : Music Data Mining
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	Serveur d'exploration sur la musique en Sarre
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Serveur d'exploration sur la musique en Sarre

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

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

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