Confronta i metodi

	Beat Tracking	Riconoscimento dell'Accordo	Estrazione della Melodia
Campo	Recupero dell'informazione musicale	Recupero dell'informazione musicale	Recupero dell'informazione musicale
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	2007	2005	2008
Ideatore	David P. Ellis	Christopher Harte	Anssi Klapuri
Tipo	Audio signal processing algorithm	Harmonic audio analysis	Polyphonic audio analysis
Fonte seminale	Ellis, D. P. (2007). Beat tracking by dynamic programming. Journal of New Music Research, 36(1), 51-60.	Harte, C., Sandler, M. B., Abdallah, S. A., & Gómez, E. (2005). Symbolic representation of musical chords: Proposed extensions to the HarmO ontology. In Proceedings of the International Society for Music Information Retrieval Conference.	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.
Alias	pulse detection, beat detection, metrical analysis	chord estimation, harmonic analysis, chord detection	pitch contour extraction, melodic line extraction, f0 tracking
Correlati	5	5	5
Sintesi	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.	Chord recognition is the task of automatically identifying the harmonic chords present in a musical recording and estimating when chord changes occur. Introduced formally by Harte et al. (2005), it is a cornerstone of music analysis and widely used in music education, cover song analysis, and musical structure understanding. Modern systems use deep learning to classify and sequence chords in real time.	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.