手法を比較

	ボーカル分離	自動音楽記譜法	ビートトラッキング
分野	音楽情報検索	音楽情報検索	音楽情報検索
系統	Machine learning	Machine learning	Machine learning
提唱年	2012	2008	2007
提唱者	Yonggang Han	Anssi Klapuri	David P. Ellis
種類	Audio source separation	Polyphonic audio-to-symbolic conversion	Audio signal processing algorithm
原典	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.	Ellis, D. P. (2007). Beat tracking by dynamic programming. Journal of New Music Research, 36(1), 51-60.
別名	singing voice extraction, voice isolation, source demixing	music-to-notation conversion, score estimation, polyphonic transcription	pulse detection, beat detection, metrical analysis
関連	5	5	5
概要	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.	Beat tracking is an algorithm for automatically identifying the temporal positions of musical beats in audio recordings. It has been widely studied since the early 2000s, particularly for rhythm analysis and music synchronization applications. The problem is central to music information retrieval and essential for music-aware systems.