Linganisha mbinu

	Algoriti ya Utambuzi wa Mlio	Usanifu wa Muziki wa Kiotomatiki
Nyanja	Upataji wa Taarifa za Muziki	Upataji wa Taarifa za Muziki
Familia	Machine learning	Machine learning
Mwaka wa asili	2002	2008
Mwanzilishi	Alain de Cheveigné	Anssi Klapuri
Aina	Fundamental frequency estimation	Polyphonic audio-to-symbolic conversion
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.	Klapuri, A. (2008). Automatic music transcription as we know it today. Journal of New Music Research, 33(3), 323-337.
Majina mbadala	f0 detection, fundamental frequency tracking, monophonic pitch extraction	music-to-notation conversion, score estimation, polyphonic transcription
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.	Automatic music transcription is the task of converting audio recordings into symbolic music notation (e.g., scores with note pitch, onset, and duration). Formalized as a research problem by Klapuri (2008), it represents one of the most challenging tasks in music information retrieval. Transcription enables music education, composition analysis, and digital preservation. Modern systems, particularly those using deep learning for piano music (Hawthorne et al., 2019), have achieved significant progress but remain far from perfect on general polyphonic music.