Methoden vergelijken

	Automatische Muziektranscriptie	Algoritme voor toonhoogtedetectie
Vakgebied	Music information retrieval	Music information retrieval
Familie	Machine learning	Machine learning
Jaar van ontstaan	2008	2002
Grondlegger	Anssi Klapuri	Alain de Cheveigné
Type	Polyphonic audio-to-symbolic conversion	Fundamental frequency estimation
Oorspronkelijke bron	Klapuri, A. (2008). Automatic music transcription as we know it today. Journal of New Music Research, 33(3), 323-337.	de Cheveigné, A., & Kawahara, H. (2002). YIN, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4), 1917-1930.
Aliassen	music-to-notation conversion, score estimation, polyphonic transcription	f0 detection, fundamental frequency tracking, monophonic pitch extraction
Verwant	5	5
Samenvatting	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.	Pitch detection (or fundamental frequency estimation) is the task of automatically determining the perceived pitch of a monophonic (single-source) audio signal at each moment in time. Formalized by de Cheveigné and Kawahara (2002) through the YIN algorithm, it is foundational to music and speech processing. Pitch detection enables vocal analysis, music transcription, instrument tuning, and speech analysis. Monophonic pitch is unambiguous; polyphonic pitch detection is fundamentally harder and a distinct problem.