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| Ackordigenkänning× | Melodieextraktion× | |
|---|---|---|
| Ämnesområde | Music information retrieval | Music information retrieval |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2005 | 2008 |
| Upphovsperson≠ | Christopher Harte | Anssi Klapuri |
| Typ≠ | Harmonic audio analysis | Polyphonic audio analysis |
| Ursprungskälla≠ | 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 ↗ | 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 ↗ |
| Alias | chord estimation, harmonic analysis, chord detection | pitch contour extraction, melodic line extraction, f0 tracking |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | 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. | 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. |
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