Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Algoritmo de Detección de Tono× | Seguimiento de pulso× | |
|---|---|---|
| Campo | Recuperación de información musical | Recuperación de información musical |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2002 | 2007 |
| Autor original≠ | Alain de Cheveigné | David P. Ellis |
| Tipo≠ | Fundamental frequency estimation | Audio signal processing algorithm |
| Fuente seminal≠ | 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 ↗ |
| Alias | f0 detection, fundamental frequency tracking, monophonic pitch extraction | pulse detection, beat detection, metrical analysis |
| Relacionados | 5 | 5 |
| Resumen≠ | 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. |
| ScholarGateConjunto de datos ↗ |
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