Bandingkan metode

	Pemisahan Vokal	Pitch Detection Algorithm
Bidang	Temu Kembali Informasi Musik	Temu Kembali Informasi Musik
Keluarga	Machine learning	Machine learning
Tahun asal	2012	2002
Pencetus	Yonggang Han	Alain de Cheveigné
Tipe	Audio source separation	Fundamental frequency estimation
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.	de Cheveigné, A., & Kawahara, H. (2002). YIN, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4), 1917-1930.
Alias	singing voice extraction, voice isolation, source demixing	f0 detection, fundamental frequency tracking, monophonic pitch extraction
Terkait	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.	Pitch detection (or fundamental frequency estimation) is the task of automatically determining the perceived pitch of a monophonic (single-source) audio signal at each moment in time. Formalized by de Cheveigné and Kawahara (2002) through the YIN algorithm, it is foundational to music and speech processing. Pitch detection enables vocal analysis, music transcription, instrument tuning, and speech analysis. Monophonic pitch is unambiguous; polyphonic pitch detection is fundamentally harder and a distinct problem.