Сравнение на методи

	Вокално разделяне	Сегментиране на музика
Област	Извличане на музикална информация	Извличане на музикална информация
Семейство	Machine learning	Machine learning
Година на възникване	2012	2001
Създател	Yonggang Han	Masataka Goto
Тип	Audio source separation	Audio structural analysis
Основополагащ източник	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.	Goto, M., & Hasegawa, Y. (2001). Automatic transcription of popular music audio. In Proceedings of the Fourth International Conference on Music Information Retrieval.
Други названия	singing voice extraction, voice isolation, source demixing	structural segmentation, music structure analysis, section boundary detection
Свързани	5	5
Резюме	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.	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.