Methoden vergleichen

	Klangfarbenanalyse	Algorithmus zur Tonhöhenerkennung
Fachgebiet	Music Information Retrieval	Music Information Retrieval
Familie	Machine learning	Machine learning
Entstehungsjahr	1977	2002
Urheber	John M. Grey	Alain de Cheveigné
Typ	Acoustic feature extraction and analysis	Fundamental frequency estimation
Wegweisende Quelle	Grey, J. M. (1977). Multidimensional perceptual scaling of musical timbres. The Journal of the Acoustical Society of America, 61(5), 1270-1277.	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.
Aliasnamen	tone color analysis, spectral characterization, timbre descriptor extraction	f0 detection, fundamental frequency tracking, monophonic pitch extraction
Verwandt	5	5
Zusammenfassung	Timbre analysis is the computational characterization and modeling of tone color—the perceived quality that distinguishes one instrument from another even at the same pitch and loudness. Pioneered by Grey (1977), timbre analysis extracts acoustic descriptors that characterize spectral shape, temporal dynamics, and harmonic content. It underlies instrument identification, music similarity assessment, and audio retrieval. Unlike melody and rhythm, timbre is high-dimensional and context-dependent, making it one of the most challenging aspects of music analysis.	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.