Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Huella de audio× | Seguimiento de pulso× | |
|---|---|---|
| Campo | Recuperación de información musical | Recuperación de información musical |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2002 | 2007 |
| Autor original≠ | Jeroen Haitsma | David P. Ellis |
| Tipo≠ | Perceptual audio hashing | Audio signal processing algorithm |
| Fuente seminal≠ | Haitsma, J., & Kalker, T. (2002). A highly robust audio fingerprinting system. In Proceedings of the International Symposium on Music Information Retrieval. link ↗ | Ellis, D. P. (2007). Beat tracking by dynamic programming. Journal of New Music Research, 36(1), 51-60. DOI ↗ |
| Alias | robust hashing, perceptual hashing, music identification | pulse detection, beat detection, metrical analysis |
| Relacionados | 5 | 5 |
| Resumen≠ | Audio fingerprinting is a technique for creating a compact, robust identifier (fingerprint) for audio recordings that uniquely represents the content while being tolerant to modifications such as compression, noise, or time-shifting. Introduced by Haitsma and Kalker (2002), it underlies music identification services like Shazam and is critical for copyright enforcement, music matching, and library deduplication. A fingerprint is not a waveform hash; it captures perceptual content and remains stable across reasonable audio alterations. | 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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