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Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Suivi du tempo× | Estimation du tempo× | |
|---|---|---|
| Domaine | Recherche d'information musicale | Recherche d'information musicale |
| Famille | Machine learning | Machine learning |
| Année d'origine≠ | 2007 | 1998 |
| Auteur d'origine≠ | David P. Ellis | Eric D. Scheirer |
| Type≠ | Audio signal processing algorithm | Audio tempo analysis |
| Source fondatrice≠ | Ellis, D. P. (2007). Beat tracking by dynamic programming. Journal of New Music Research, 36(1), 51-60. DOI ↗ | Scheirer, E. D. (1998). Tempo and beat analysis of acoustic musical signals. The Journal of the Acoustical Society of America, 103(1), 588-601. DOI ↗ |
| Alias | pulse detection, beat detection, metrical analysis | tempo detection, BPM estimation, pulse rate detection |
| Apparentées | 5 | 5 |
| Résumé≠ | Beat tracking is an algorithm for automatically identifying the temporal positions of musical beats in audio recordings. It has been widely studied since the early 2000s, particularly for rhythm analysis and music synchronization applications. The problem is central to music information retrieval and essential for music-aware systems. | Tempo estimation is the task of automatically determining the beats per minute (BPM) or tempo of a musical recording. Introduced by Scheirer (1998), it is fundamental to rhythm analysis, music classification, and synchronization applications. Tempo is one of the most perceptually salient features of music; accurate estimation enables music-aware systems and human-machine interaction. Unlike beat tracking, which produces discrete beat times, tempo estimation yields a single BPM value (or a distribution of likely tempi). |
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