Methoden vergleichen

	Algorithmus zur Tonhöhenerkennung	Automatische Musiktranskription
Fachgebiet	Music Information Retrieval	Music Information Retrieval
Familie	Machine learning	Machine learning
Entstehungsjahr	2002	2008
Urheber	Alain de Cheveigné	Anssi Klapuri
Typ	Fundamental frequency estimation	Polyphonic audio-to-symbolic conversion
Wegweisende Quelle	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.
Aliasnamen	f0 detection, fundamental frequency tracking, monophonic pitch extraction	music-to-notation conversion, score estimation, polyphonic transcription
Verwandt	5	5
Zusammenfassung	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.