Porovnat metody
Prohlédněte si vybrané metody vedle sebe; řádky, které se liší, jsou zvýrazněny.
| Algoritmus detekce výšky tónu× | Sledování beatu× | |
|---|---|---|
| Obor | Vyhledávání hudebních informací | Vyhledávání hudebních informací |
| Rodina | Machine learning | Machine learning |
| Rok vzniku≠ | 2002 | 2007 |
| Tvůrce≠ | Alain de Cheveigné | David P. Ellis |
| Typ≠ | Fundamental frequency estimation | Audio signal processing algorithm |
| Původní zdroj≠ | 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. DOI ↗ | Ellis, D. P. (2007). Beat tracking by dynamic programming. Journal of New Music Research, 36(1), 51-60. DOI ↗ |
| Další názvy | f0 detection, fundamental frequency tracking, monophonic pitch extraction | pulse detection, beat detection, metrical analysis |
| Příbuzné | 5 | 5 |
| Shrnutí≠ | 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. | Beat tracking is an algorithm for automatically identifying the temporal positions of musical beats in audio recordings. It has been widely studied since the early 2000s, particularly for rhythm analysis and music synchronization applications. The problem is central to music information retrieval and essential for music-aware systems. |
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