Comparer des méthodes

	Transcription automatique de musique	Suivi du tempo	Extraction de mélodie	Segmentation musicale
Domaine	Recherche d'information musicale	Recherche d'information musicale	Recherche d'information musicale	Recherche d'information musicale
Famille	Machine learning	Machine learning	Machine learning	Machine learning
Année d'origine	2008	2007	2008	2001
Auteur d'origine	Anssi Klapuri	David P. Ellis	Anssi Klapuri	Masataka Goto
Type	Polyphonic audio-to-symbolic conversion	Audio signal processing algorithm	Polyphonic audio analysis	Audio structural analysis
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.	Salamon, J., & Gómez, E. (2014). Melody extraction from polyphonic music signals using pitch contour characteristics. IEEE Transactions on Audio, Speech, and Language Processing, 20(6), 1759-1770.	Goto, M., & Hasegawa, Y. (2001). Automatic transcription of popular music audio. In Proceedings of the Fourth International Conference on Music Information Retrieval.
Alias	music-to-notation conversion, score estimation, polyphonic transcription	pulse detection, beat detection, metrical analysis	pitch contour extraction, melodic line extraction, f0 tracking	structural segmentation, music structure analysis, section boundary detection
Apparentées	5	5	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.	Melody extraction is the task of automatically isolating the main melodic contour from polyphonic music recordings. It originated from music transcription research in the 2000s and addresses the core challenge of human pitch perception: identifying the perceptually dominant pitch when many instruments play simultaneously. Modern approaches use deep learning and are essential for music analysis, cover song detection, and music-to-lyrics alignment.	Music segmentation is the task of dividing a musical recording into distinct structural sections (e.g., verse, chorus, bridge, pre-chorus, outro). Introduced by Goto (2001), it identifies major structural boundaries and labels sections according to musical form. Segmentation is essential for music understanding, audio editing, and composition analysis. It enables higher-level tasks like cover song identification and song structure-aware music generation.