Methoden vergleichen

	Audio Fingerprinting	Algorithmus zur Tonhöhenerkennung
Fachgebiet	Music Information Retrieval	Music Information Retrieval
Familie	Machine learning	Machine learning
Entstehungsjahr	2002	2002
Urheber	Jeroen Haitsma	Alain de Cheveigné
Typ	Perceptual audio hashing	Fundamental frequency estimation
Wegweisende Quelle	Haitsma, J., & Kalker, T. (2002). A highly robust audio fingerprinting system. In Proceedings of the International Symposium on Music Information Retrieval.	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	robust hashing, perceptual hashing, music identification	f0 detection, fundamental frequency tracking, monophonic pitch extraction
Verwandt	5	5
Zusammenfassung	Audio fingerprinting is a technique for creating a compact, robust identifier (fingerprint) for audio recordings that uniquely represents the content while being tolerant to modifications such as compression, noise, or time-shifting. Introduced by Haitsma and Kalker (2002), it underlies music identification services like Shazam and is critical for copyright enforcement, music matching, and library deduplication. A fingerprint is not a waveform hash; it captures perceptual content and remains stable across reasonable audio alterations.	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.