Comparar métodos

	Huella de audio	Segmentación musical
Campo	Recuperación de información musical	Recuperación de información musical
Familia	Machine learning	Machine learning
Año de origen	2002	2001
Autor original	Jeroen Haitsma	Masataka Goto
Tipo	Perceptual audio hashing	Audio structural analysis
Fuente seminal	Haitsma, J., & Kalker, T. (2002). A highly robust audio fingerprinting system. In Proceedings of the International Symposium on Music Information Retrieval.	Goto, M., & Hasegawa, Y. (2001). Automatic transcription of popular music audio. In Proceedings of the Fourth International Conference on Music Information Retrieval.
Alias	robust hashing, perceptual hashing, music identification	structural segmentation, music structure analysis, section boundary detection
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.	Music segmentation is the task of dividing a musical recording into distinct structural sections (e.g., verse, chorus, bridge, pre-chorus, outro). Introduced by Goto (2001), it identifies major structural boundaries and labels sections according to musical form. Segmentation is essential for music understanding, audio editing, and composition analysis. It enables higher-level tasks like cover song identification and song structure-aware music generation.