Linganisha mbinu

	Algoriti ya Utambuzi wa Mlio	Utengano wa Sauti (Vocal Separation)
Nyanja	Upataji wa Taarifa za Muziki	Upataji wa Taarifa za Muziki
Familia	Machine learning	Machine learning
Mwaka wa asili	2002	2012
Mwanzilishi	Alain de Cheveigné	Yonggang Han
Aina	Fundamental frequency estimation	Audio source separation
Chanzo asilia	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.	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.
Majina mbadala	f0 detection, fundamental frequency tracking, monophonic pitch extraction	singing voice extraction, voice isolation, source demixing
Zinazohusiana	5	5
Muhtasari	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.	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.