Comparer des méthodes

	Transcription automatique de musique	Algorithme de détection de hauteur
Domaine	Recherche d'information musicale	Recherche d'information musicale
Famille	Machine learning	Machine learning
Année d'origine	2008	2002
Auteur d'origine	Anssi Klapuri	Alain de Cheveigné
Type	Polyphonic audio-to-symbolic conversion	Fundamental frequency estimation
Source fondatrice	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.
Alias	music-to-notation conversion, score estimation, polyphonic transcription	f0 detection, fundamental frequency tracking, monophonic pitch extraction
Apparentées	5	5
Résumé	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.