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| Pengenalan Instrumen× | Segmentasi Musik× | |
|---|---|---|
| Bidang | Temu Kembali Informasi Musik | Temu Kembali Informasi Musik |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2005 | 2001 |
| Pencetus≠ | Antti Eronen | Masataka Goto |
| Tipe≠ | Timbre-based audio classification | Audio structural analysis |
| Sumber perintis≠ | Eronen, A., Peltonen, V., Tuomi, J., Klapuri, A., Fagerlund, S., Sorsa, T., & Lorho, G. (2005). Audio-based context recognition. IEEE Transactions on Audio, Speech, and Language Processing, 14(1), 321-329. DOI ↗ | Goto, M., & Hasegawa, Y. (2001). Automatic transcription of popular music audio. In Proceedings of the Fourth International Conference on Music Information Retrieval. link ↗ |
| Alias | instrument classification, timbre identification, instrument detection | structural segmentation, music structure analysis, section boundary detection |
| Terkait | 5 | 5 |
| Ringkasan≠ | Instrument recognition is the task of automatically identifying which musical instruments are present in an audio recording. Formalized by Eronen et al. (2005), it addresses timbre—the tonal quality distinguishing one instrument from another. Instrument recognition is essential for music analysis, transcription, automatic indexing, and music education. It remains challenging in polyphonic contexts but has achieved good accuracy in solo and sparse accompaniment scenarios. | 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. |
| ScholarGateSet data ↗ |
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