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| Musikgenre-Klassifikation× | Musiksegmentierung× | |
|---|---|---|
| Fachgebiet | Music Information Retrieval | Music Information Retrieval |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2002 | 2001 |
| Urheber≠ | George Tzanetakis | Masataka Goto |
| Typ≠ | Audio feature-based classification | Audio structural analysis |
| Wegweisende Quelle≠ | Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293-302. DOI ↗ | Goto, M., & Hasegawa, Y. (2001). Automatic transcription of popular music audio. In Proceedings of the Fourth International Conference on Music Information Retrieval. link ↗ |
| Aliasnamen | genre recognition, music categorization, style classification | structural segmentation, music structure analysis, section boundary detection |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Music genre classification is the task of automatically assigning genre labels (rock, jazz, classical, pop, etc.) to audio recordings. Introduced formally by Tzanetakis and Cook (2002), it is one of the earliest and most studied music information retrieval problems. It remains critical for music discovery, recommendation systems, digital library organization, and music streaming services. Modern systems achieve high accuracy on standard datasets using deep learning. | Music segmentation is the task of dividing a musical recording into distinct structural sections (e.g., verse, chorus, bridge, pre-chorus, outro). Introduced by Goto (2001), it identifies major structural boundaries and labels sections according to musical form. Segmentation is essential for music understanding, audio editing, and composition analysis. It enables higher-level tasks like cover song identification and song structure-aware music generation. |
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