Comparer des méthodes

	Transcription automatique de musique	Suivi du tempo
Domaine	Recherche d'information musicale	Recherche d'information musicale
Famille	Machine learning	Machine learning
Année d'origine	2008	2007
Auteur d'origine	Anssi Klapuri	David P. Ellis
Type	Polyphonic audio-to-symbolic conversion	Audio signal processing algorithm
Source fondatrice	Klapuri, A. (2008). Automatic music transcription as we know it today. Journal of New Music Research, 33(3), 323-337.	Ellis, D. P. (2007). Beat tracking by dynamic programming. Journal of New Music Research, 36(1), 51-60.
Alias	music-to-notation conversion, score estimation, polyphonic transcription	pulse detection, beat detection, metrical analysis
Apparentées	5	5
Résumé	Automatic music transcription is the task of converting audio recordings into symbolic music notation (e.g., scores with note pitch, onset, and duration). Formalized as a research problem by Klapuri (2008), it represents one of the most challenging tasks in music information retrieval. Transcription enables music education, composition analysis, and digital preservation. Modern systems, particularly those using deep learning for piano music (Hawthorne et al., 2019), have achieved significant progress but remain far from perfect on general polyphonic music.	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.