Bandingkan kaedah

	Pemisahan Vokal	Pengekstrakan Melodi	Segmentasi Muzik
Bidang	Capaian Maklumat Muzik	Capaian Maklumat Muzik	Capaian Maklumat Muzik
Keluarga	Machine learning	Machine learning	Machine learning
Tahun asal	2012	2008	2001
Pengasas	Yonggang Han	Anssi Klapuri	Masataka Goto
Jenis	Audio source separation	Polyphonic audio analysis	Audio structural analysis
Sumber perintis	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.	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.
Alias	singing voice extraction, voice isolation, source demixing	pitch contour extraction, melodic line extraction, f0 tracking	structural segmentation, music structure analysis, section boundary detection
Berkaitan	5	5	5
Ringkasan	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.	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.