Comparer des méthodes

	Séparation Vocale	Transcription automatique de musique
Domaine	Recherche d'information musicale	Recherche d'information musicale
Famille	Machine learning	Machine learning
Année d'origine	2012	2008
Auteur d'origine	Yonggang Han	Anssi Klapuri
Type	Audio source separation	Polyphonic audio-to-symbolic conversion
Source fondatrice	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.	Klapuri, A. (2008). Automatic music transcription as we know it today. Journal of New Music Research, 33(3), 323-337.
Alias	singing voice extraction, voice isolation, source demixing	music-to-notation conversion, score estimation, polyphonic transcription
Apparentées	5	5
Résumé	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.	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.