方法对比
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| 自动音乐转录× | 和弦识别× | |
|---|---|---|
| 领域 | 音乐信息检索 | 音乐信息检索 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2008 | 2005 |
| 提出者≠ | Anssi Klapuri | Christopher Harte |
| 类型≠ | Polyphonic audio-to-symbolic conversion | Harmonic audio analysis |
| 开创性文献≠ | Klapuri, A. (2008). Automatic music transcription as we know it today. Journal of New Music Research, 33(3), 323-337. DOI ↗ | Harte, C., Sandler, M. B., Abdallah, S. A., & Gómez, E. (2005). Symbolic representation of musical chords: Proposed extensions to the HarmO ontology. In Proceedings of the International Society for Music Information Retrieval Conference. link ↗ |
| 别名 | music-to-notation conversion, score estimation, polyphonic transcription | chord estimation, harmonic analysis, chord detection |
| 相关 | 5 | 5 |
| 摘要≠ | 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. | Chord recognition is the task of automatically identifying the harmonic chords present in a musical recording and estimating when chord changes occur. Introduced formally by Harte et al. (2005), it is a cornerstone of music analysis and widely used in music education, cover song analysis, and musical structure understanding. Modern systems use deep learning to classify and sequence chords in real time. |
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