Linganisha mbinu

	Utengano wa Sauti (Vocal Separation)	Beat Tracking	Uchimbaji wa Melodi	Mgawanyo wa Muziki
Nyanja	Upataji wa Taarifa za Muziki	Upataji wa Taarifa za Muziki	Upataji wa Taarifa za Muziki	Upataji wa Taarifa za Muziki
Familia	Machine learning	Machine learning	Machine learning	Machine learning
Mwaka wa asili	2012	2007	2008	2001
Mwanzilishi	Yonggang Han	David P. Ellis	Anssi Klapuri	Masataka Goto
Aina	Audio source separation	Audio signal processing algorithm	Polyphonic audio analysis	Audio structural analysis
Chanzo asilia	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.	Goto, M., & Hasegawa, Y. (2001). Automatic transcription of popular music audio. In Proceedings of the Fourth International Conference on Music Information Retrieval.
Majina mbadala	singing voice extraction, voice isolation, source demixing	pulse detection, beat detection, metrical analysis	pitch contour extraction, melodic line extraction, f0 tracking	structural segmentation, music structure analysis, section boundary detection
Zinazohusiana	5	5	5	5
Muhtasari	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.	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.