Methoden vergleichen

	Algorithmus zur Tonhöhenerkennung	Vokaltrennung
Fachgebiet	Music Information Retrieval	Music Information Retrieval
Familie	Machine learning	Machine learning
Entstehungsjahr	2002	2012
Urheber	Alain de Cheveigné	Yonggang Han
Typ	Fundamental frequency estimation	Audio source separation
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.	Han, Y., Qin, Z., & Kang, Z. (2012). Singing voice separation using spectral floor filtered spectrograms. In Proceedings of the International Society for Music Information Retrieval Conference.
Aliasnamen	f0 detection, fundamental frequency tracking, monophonic pitch extraction	singing voice extraction, voice isolation, source demixing
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.	Vocal separation is the task of isolating the singing voice from a mixed music recording, leaving the instrumental accompaniment. Introduced formally by Han et al. (2012), it is critical for music editing, remixing, karaoke generation, and music analysis. Modern deep learning approaches (Défossez et al., 2021) have achieved impressive quality, enabling practical applications in music production and streaming services. Vocal separation is a special case of source separation, where the goal is to isolate the most perceptually salient source.