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| 自动音乐转录× | 节拍跟踪× | |
|---|---|---|
| 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 | 2007 |
| 提出者≠ | Anssi Klapuri | David P. Ellis |
| 类型≠ | Polyphonic audio-to-symbolic conversion | Audio signal processing algorithm |
| 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 ↗ | Ellis, D. P. (2007). Beat tracking by dynamic programming. Journal of New Music Research, 36(1), 51-60. DOI ↗ |
| 别名 | music-to-notation conversion, score estimation, polyphonic transcription | pulse detection, beat detection, metrical analysis |
| 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. | 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. |
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