Comparer des méthodes

	Suivi du tempo	Extraction de mélodie	Classification de genres musicaux
Domaine	Recherche d'information musicale	Recherche d'information musicale	Recherche d'information musicale
Famille	Machine learning	Machine learning	Machine learning
Année d'origine	2007	2008	2002
Auteur d'origine	David P. Ellis	Anssi Klapuri	George Tzanetakis
Type	Audio signal processing algorithm	Polyphonic audio analysis	Audio feature-based classification
Source fondatrice	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.	Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293-302.
Alias	pulse detection, beat detection, metrical analysis	pitch contour extraction, melodic line extraction, f0 tracking	genre recognition, music categorization, style classification
Apparentées	5	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.	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 genre classification is the task of automatically assigning genre labels (rock, jazz, classical, pop, etc.) to audio recordings. Introduced formally by Tzanetakis and Cook (2002), it is one of the earliest and most studied music information retrieval problems. It remains critical for music discovery, recommendation systems, digital library organization, and music streaming services. Modern systems achieve high accuracy on standard datasets using deep learning.