Сравнение на методи

	Разпознаване на акорди	Алгоритъм за детекция на височина на тона
Област	Извличане на музикална информация	Извличане на музикална информация
Семейство	Machine learning	Machine learning
Година на възникване	2005	2002
Създател	Christopher Harte	Alain de Cheveigné
Тип	Harmonic audio analysis	Fundamental frequency estimation
Основополагащ източник	Harte, C., Sandler, M. B., Abdallah, S. A., & Gómez, E. (2005). Symbolic representation of musical chords: Proposed extensions to the HarmO ontology. In Proceedings of the International Society for Music Information Retrieval Conference.	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.
Други названия	chord estimation, harmonic analysis, chord detection	f0 detection, fundamental frequency tracking, monophonic pitch extraction
Свързани	5	5
Резюме	Chord recognition is the task of automatically identifying the harmonic chords present in a musical recording and estimating when chord changes occur. Introduced formally by Harte et al. (2005), it is a cornerstone of music analysis and widely used in music education, cover song analysis, and musical structure understanding. Modern systems use deep learning to classify and sequence chords in real time.	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.