Usporedite metode

	Automatska glazbena transkripcija	Praćenje takta
Područje	Pronalaženje glazbenih informacija	Pronalaženje glazbenih informacija
Obitelj	Machine learning	Machine learning
Godina nastanka	2008	2007
Tvorac	Anssi Klapuri	David P. Ellis
Vrsta	Polyphonic audio-to-symbolic conversion	Audio signal processing algorithm
Temeljni izvor	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.
Drugi nazivi	music-to-notation conversion, score estimation, polyphonic transcription	pulse detection, beat detection, metrical analysis
Srodne	5	5
Sažetak	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.