Methoden vergleichen

	Tempo-Schätzung	Algorithmus zur Tonhöhenerkennung
Fachgebiet	Music Information Retrieval	Music Information Retrieval
Familie	Machine learning	Machine learning
Entstehungsjahr	1998	2002
Urheber	Eric D. Scheirer	Alain de Cheveigné
Typ	Audio tempo analysis	Fundamental frequency estimation
Wegweisende Quelle	Scheirer, E. D. (1998). Tempo and beat analysis of acoustic musical signals. The Journal of the Acoustical Society of America, 103(1), 588-601.	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	tempo detection, BPM estimation, pulse rate detection	f0 detection, fundamental frequency tracking, monophonic pitch extraction
Verwandt	5	5
Zusammenfassung	Tempo estimation is the task of automatically determining the beats per minute (BPM) or tempo of a musical recording. Introduced by Scheirer (1998), it is fundamental to rhythm analysis, music classification, and synchronization applications. Tempo is one of the most perceptually salient features of music; accurate estimation enables music-aware systems and human-machine interaction. Unlike beat tracking, which produces discrete beat times, tempo estimation yields a single BPM value (or a distribution of likely tempi).	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.