Võrdle meetodeid

	Vokaalide eraldamine	Taktilöökide tuvastamine	Meloodia eraldamine
Valdkond	Muusikainfo otsing	Muusikainfo otsing	Muusikainfo otsing
Perekond	Machine learning	Machine learning	Machine learning
Tekkeaasta	2012	2007	2008
Looja	Yonggang Han	David P. Ellis	Anssi Klapuri
Tüüp	Audio source separation	Audio signal processing algorithm	Polyphonic audio analysis
Algallikas	Han, Y., Qin, Z., & Kang, Z. (2012). Singing voice separation using spectral floor filtered spectrograms. In Proceedings of the International Society for Music Information Retrieval Conference.	Ellis, D. P. (2007). Beat tracking by dynamic programming. Journal of New Music Research, 36(1), 51-60.	Salamon, J., & Gómez, E. (2014). Melody extraction from polyphonic music signals using pitch contour characteristics. IEEE Transactions on Audio, Speech, and Language Processing, 20(6), 1759-1770.
Rööpnimetused	singing voice extraction, voice isolation, source demixing	pulse detection, beat detection, metrical analysis	pitch contour extraction, melodic line extraction, f0 tracking
Seotud	5	5	5
Kokkuvõte	Vocal separation is the task of isolating the singing voice from a mixed music recording, leaving the instrumental accompaniment. Introduced formally by Han et al. (2012), it is critical for music editing, remixing, karaoke generation, and music analysis. Modern deep learning approaches (Défossez et al., 2021) have achieved impressive quality, enabling practical applications in music production and streaming services. Vocal separation is a special case of source separation, where the goal is to isolate the most perceptually salient source.	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.	Melody extraction is the task of automatically isolating the main melodic contour from polyphonic music recordings. It originated from music transcription research in the 2000s and addresses the core challenge of human pitch perception: identifying the perceptually dominant pitch when many instruments play simultaneously. Modern approaches use deep learning and are essential for music analysis, cover song detection, and music-to-lyrics alignment.