Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Vokaalide eraldamine× | Muusika segmenteerimine× | |
|---|---|---|
| Valdkond | Muusikainfo otsing | Muusikainfo otsing |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2012 | 2001 |
| Looja≠ | Yonggang Han | Masataka Goto |
| Tüüp≠ | Audio source separation | Audio structural analysis |
| Algallikas≠ | Han, Y., Qin, Z., & Kang, Z. (2012). Singing voice separation using spectral floor filtered spectrograms. In Proceedings of the International Society for Music Information Retrieval Conference. link ↗ | Goto, M., & Hasegawa, Y. (2001). Automatic transcription of popular music audio. In Proceedings of the Fourth International Conference on Music Information Retrieval. link ↗ |
| Rööpnimetused | singing voice extraction, voice isolation, source demixing | structural segmentation, music structure analysis, section boundary detection |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | Vocal separation is the task of isolating the singing voice from a mixed music recording, leaving the instrumental accompaniment. Introduced formally by Han et al. (2012), it is critical for music editing, remixing, karaoke generation, and music analysis. Modern deep learning approaches (Défossez et al., 2021) have achieved impressive quality, enabling practical applications in music production and streaming services. Vocal separation is a special case of source separation, where the goal is to isolate the most perceptually salient source. | 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. |
| ScholarGateAndmestik ↗ |
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