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	Beat Tracking	Riconoscimento dell'Accordo	Classificazione del Genere Musicale
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	2002
Ideatore	David P. Ellis	Christopher Harte	George Tzanetakis
Tipo	Audio signal processing algorithm	Harmonic audio analysis	Audio feature-based classification
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.	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	chord estimation, harmonic analysis, chord detection	genre recognition, music categorization, style classification
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.	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.