Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Algoritms skaņas augstuma noteikšanai× | Ritma izsekošana× | |
|---|---|---|
| Nozare | Mūzikas informācijas izgūšana | Mūzikas informācijas izgūšana |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2002 | 2007 |
| Autors≠ | Alain de Cheveigné | David P. Ellis |
| Tips≠ | Fundamental frequency estimation | Audio signal processing algorithm |
| Pirmavots≠ | 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 ↗ |
| Citi nosaukumi | f0 detection, fundamental frequency tracking, monophonic pitch extraction | pulse detection, beat detection, metrical analysis |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. |
| ScholarGateDatu kopa ↗ |
|
|