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| 멜로디 추출× | 음악에서의 화성 분석× | |
|---|---|---|
| 분야 | 음악 정보 검색 | 음악 정보 검색 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2008 | 2002 |
| 창시자≠ | Anssi Klapuri | Bryan Pardo |
| 유형≠ | Polyphonic audio analysis | Harmonic function and progression analysis |
| 원전≠ | Salamon, J., & Gómez, E. (2014). Melody extraction from polyphonic music signals using pitch contour characteristics. IEEE Transactions on Audio, Speech, and Language Processing, 20(6), 1759-1770. link ↗ | Pardo, B., & Birmingham, W. P. (2002). Algorithms for chordal analysis. Computer Music Journal, 26(4), 27-49. DOI ↗ |
| 별칭 | pitch contour extraction, melodic line extraction, f0 tracking | functional harmony analysis, harmonic progression detection, tonal function estimation |
| 관련 | 5 | 5 |
| 요약≠ | Melody extraction is the task of automatically isolating the main melodic contour from polyphonic music recordings. It originated from music transcription research in the 2000s and addresses the core challenge of human pitch perception: identifying the perceptually dominant pitch when many instruments play simultaneously. Modern approaches use deep learning and are essential for music analysis, cover song detection, and music-to-lyrics alignment. | Harmonic analysis is the computational study of chord progressions, harmonic function, and tonal relationships in music. Formalized for audio by Pardo and Birmingham (2002), it goes beyond simple chord identification to interpret harmonic role and structure. Harmonic analysis is essential for music theory education, compositional understanding, and music generation systems. It requires understanding both the chords themselves and their functional relationships within a tonal context. |
| ScholarGate데이터셋 ↗ |
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