Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Musikalsk toneartdeteksjon× | Algoritme for tonehøyde-deteksjon× | |
|---|---|---|
| Fagfelt | Musikkinformasjonsgjenfinning | Musikkinformasjonsgjenfinning |
| Familie | Machine learning | Machine learning |
| Opprinnelsesår≠ | 2006 | 2002 |
| Opphavsperson≠ | Emilia Gómez | Alain de Cheveigné |
| Type≠ | Tonal center estimation | Fundamental frequency estimation |
| Opprinnelig kilde≠ | Gómez, E. (2006). Tonal description of polyphonic audio for music content processing. In INESC Porto PhD Thesis. link ↗ | 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 ↗ |
| Alias | key recognition, tonality estimation, musical center detection | f0 detection, fundamental frequency tracking, monophonic pitch extraction |
| Relaterte | 5 | 5 |
| Sammendrag≠ | 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. | 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. |
| ScholarGateDatasett ↗ |
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