Сравнение методов

	Гармонический анализ в музыке	Алгоритм определения высоты тона
Область	Извлечение музыкальной информации	Извлечение музыкальной информации
Семейство	Machine learning	Machine learning
Год появления	2002	2002
Автор метода	Bryan Pardo	Alain de Cheveigné
Тип	Harmonic function and progression analysis	Fundamental frequency estimation
Основополагающий источник	Pardo, B., & Birmingham, W. P. (2002). Algorithms for chordal analysis. Computer Music Journal, 26(4), 27-49.	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.
Другие названия	functional harmony analysis, harmonic progression detection, tonal function estimation	f0 detection, fundamental frequency tracking, monophonic pitch extraction
Связанные	5	5
Сводка	Harmonic analysis is the computational study of chord progressions, harmonic function, and tonal relationships in music. Formalized for audio by Pardo and Birmingham (2002), it goes beyond simple chord identification to interpret harmonic role and structure. Harmonic analysis is essential for music theory education, compositional understanding, and music generation systems. It requires understanding both the chords themselves and their functional relationships within a tonal context.	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.