Σύγκριση μεθόδων

	Αλγόριθμος Ανίχνευσης Τόνου	Αυτόματη μεταγραφή μουσικής
Πεδίο	Ανάκτηση Μουσικής Πληροφορίας	Ανάκτηση Μουσικής Πληροφορίας
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2002	2008
Δημιουργός	Alain de Cheveigné	Anssi Klapuri
Τύπος	Fundamental frequency estimation	Polyphonic audio-to-symbolic conversion
Θεμελιώδης πηγή	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.	Klapuri, A. (2008). Automatic music transcription as we know it today. Journal of New Music Research, 33(3), 323-337.
Εναλλακτικές ονομασίες	f0 detection, fundamental frequency tracking, monophonic pitch extraction	music-to-notation conversion, score estimation, polyphonic transcription
Συναφείς	5	5
Σύνοψη	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.	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.