Methoden vergleichen

	Instrumentenerkennung	Algorithmus zur Tonhöhenerkennung
Fachgebiet	Music Information Retrieval	Music Information Retrieval
Familie	Machine learning	Machine learning
Entstehungsjahr	2005	2002
Urheber	Antti Eronen	Alain de Cheveigné
Typ	Timbre-based audio classification	Fundamental frequency estimation
Wegweisende Quelle	Eronen, A., Peltonen, V., Tuomi, J., Klapuri, A., Fagerlund, S., Sorsa, T., & Lorho, G. (2005). Audio-based context recognition. IEEE Transactions on Audio, Speech, and Language Processing, 14(1), 321-329.	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	instrument classification, timbre identification, instrument detection	f0 detection, fundamental frequency tracking, monophonic pitch extraction
Verwandt	5	5
Zusammenfassung	Instrument recognition is the task of automatically identifying which musical instruments are present in an audio recording. Formalized by Eronen et al. (2005), it addresses timbre—the tonal quality distinguishing one instrument from another. Instrument recognition is essential for music analysis, transcription, automatic indexing, and music education. It remains challenging in polyphonic contexts but has achieved good accuracy in solo and sparse accompaniment scenarios.	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.