Ensemble size classification in Colombian Andean string music recordings
Reliable methods for automatic retrieval of semantic information from large digital music archives can play a critical role in musicological research and musical heritage preservation. With the advancement of machine learning techniques, new possibilities for information retrieval in scenarios where...
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| Veröffentlicht in: | CMMR (14. : 2019 : Marseille) Perception, representations, image, sound, music |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , , |
| Format: | UnknownFormat |
| Sprache: | eng |
| Veröffentlicht: |
2021
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Tag hinzufügen | Zusammenfassung: | Reliable methods for automatic retrieval of semantic information from large digital music archives can play a critical role in musicological research and musical heritage preservation. With the advancement of machine learning techniques, new possibilities for information retrieval in scenarios where ground-truth data is scarce are now available. This work investigates the problem of ensemble size classification in music recordings. For this purpose, a new dataset of Colombian Andean string music was compiled and annotated by musicological experts. Different neural network architectures, as well as pre-processing steps and data augmentation techniques were systematically evaluated and optimized. The best deep neural network architecture achieved 81.5% file-wise mean class accuracy using only feed forward layers with linear magnitude spectrograms as input representation. This model will serve as a baseline for future research on ensemble size classification. |
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| ISBN: | 9783030702090 |