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| Instrumentgenkendelse× | Musikgenreklassifikation× | |
|---|---|---|
| Fagområde | Musikinformationssøgning | Musikinformationssøgning |
| Familie | Machine learning | Machine learning |
| Oprindelsesår≠ | 2005 | 2002 |
| Ophavsperson≠ | Antti Eronen | George Tzanetakis |
| Type≠ | Timbre-based audio classification | Audio feature-based classification |
| Oprindelig kilde≠ | 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 ↗ | Tzanetakis, G., & Cook, P. (2002). Musical genre classification of audio signals. IEEE Transactions on Speech and Audio Processing, 10(5), 293-302. DOI ↗ |
| Aliasser | instrument classification, timbre identification, instrument detection | genre recognition, music categorization, style classification |
| Relaterede | 5 | 5 |
| Resumé≠ | 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 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. |
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