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| 악기 인식× | 자동 음악 채보× | |
|---|---|---|
| 분야 | 음악 정보 검색 | 음악 정보 검색 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2005 | 2008 |
| 창시자≠ | Antti Eronen | Anssi Klapuri |
| 유형≠ | Timbre-based audio classification | Polyphonic audio-to-symbolic conversion |
| 원전≠ | Eronen, A., Peltonen, V., Tuomi, J., Klapuri, A., Fagerlund, S., Sorsa, T., & Lorho, G. (2005). Audio-based context recognition. IEEE Transactions on Audio, Speech, and Language Processing, 14(1), 321-329. DOI ↗ | Klapuri, A. (2008). Automatic music transcription as we know it today. Journal of New Music Research, 33(3), 323-337. DOI ↗ |
| 별칭 | instrument classification, timbre identification, instrument detection | music-to-notation conversion, score estimation, polyphonic transcription |
| 관련 | 5 | 5 |
| 요약≠ | Instrument recognition is the task of automatically identifying which musical instruments are present in an audio recording. Formalized by Eronen et al. (2005), it addresses timbre—the tonal quality distinguishing one instrument from another. Instrument recognition is essential for music analysis, transcription, automatic indexing, and music education. It remains challenging in polyphonic contexts but has achieved good accuracy in solo and sparse accompaniment scenarios. | 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. |
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