Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Sävelkorkeuden tunnistusalgoritmi× | Sävellajin tunnistus× | |
|---|---|---|
| Tieteenala | Musiikin tiedonhaku | Musiikin tiedonhaku |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2002 | 2006 |
| Kehittäjä≠ | Alain de Cheveigné | Emilia Gómez |
| Tyyppi≠ | Fundamental frequency estimation | Tonal center estimation |
| Alkuperäislähde≠ | 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 ↗ | Gómez, E. (2006). Tonal description of polyphonic audio for music content processing. In INESC Porto PhD Thesis. link ↗ |
| Rinnakkaisnimet | f0 detection, fundamental frequency tracking, monophonic pitch extraction | key recognition, tonality estimation, musical center detection |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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. | Musical key detection is the task of automatically determining the key (tonal center) and scale mode of a musical composition from its audio. Introduced formally by Gómez (2006), it is essential for music analysis, transposition, harmonic understanding, and music theory education. The key defines the tonal center around which a piece gravitates; identifying it enables deeper structural understanding. Key detection is closely related to chord recognition but operates at a higher level of abstraction. |
| ScholarGateAineisto ↗ |
|
|