Compară metode

	Extragerea melodiei	Algoritm de Detecție a Înălțimii Tonului
Domeniu	Regăsirea informației muzicale	Regăsirea informației muzicale
Familie	Machine learning	Machine learning
Anul apariției	2008	2002
Autorul original	Anssi Klapuri	Alain de Cheveigné
Tip	Polyphonic audio analysis	Fundamental frequency estimation
Sursa seminală	Salamon, J., & Gómez, E. (2014). Melody extraction from polyphonic music signals using pitch contour characteristics. IEEE Transactions on Audio, Speech, and Language Processing, 20(6), 1759-1770.	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.
Denumiri alternative	pitch contour extraction, melodic line extraction, f0 tracking	f0 detection, fundamental frequency tracking, monophonic pitch extraction
Înrudite	5	5
Rezumat	Melody extraction is the task of automatically isolating the main melodic contour from polyphonic music recordings. It originated from music transcription research in the 2000s and addresses the core challenge of human pitch perception: identifying the perceptually dominant pitch when many instruments play simultaneously. Modern approaches use deep learning and are essential for music analysis, cover song detection, and music-to-lyrics alignment.	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.