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| Ackordigenkänning× | Algoritm för tonhöjdsdetektering× | |
|---|---|---|
| Ämnesområde | Music information retrieval | Music information retrieval |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2005 | 2002 |
| Upphovsperson≠ | Christopher Harte | Alain de Cheveigné |
| Typ≠ | Harmonic audio analysis | Fundamental frequency estimation |
| 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 ↗ | de Cheveigné, A., & Kawahara, H. (2002). YIN, a fundamental frequency estimator for speech and music. The Journal of the Acoustical Society of America, 111(4), 1917-1930. DOI ↗ |
| Alias | chord estimation, harmonic analysis, chord detection | f0 detection, fundamental frequency tracking, monophonic pitch extraction |
| 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. | Pitch detection (or fundamental frequency estimation) is the task of automatically determining the perceived pitch of a monophonic (single-source) audio signal at each moment in time. Formalized by de Cheveigné and Kawahara (2002) through the YIN algorithm, it is foundational to music and speech processing. Pitch detection enables vocal analysis, music transcription, instrument tuning, and speech analysis. Monophonic pitch is unambiguous; polyphonic pitch detection is fundamentally harder and a distinct problem. |
| ScholarGateDatamängd ↗ |
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