Autor: |
Garcia, Hugo Flores, Aguilar, Aldo, Manilow, Ethan, Pardo, Bryan |
Rok vydání: |
2021 |
Předmět: |
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Druh dokumentu: |
Working Paper |
Popis: |
Deep learning work on musical instrument recognition has generally focused on instrument classes for which we have abundant data. In this work, we exploit hierarchical relationships between instruments in a few-shot learning setup to enable classification of a wider set of musical instruments, given a few examples at inference. We apply a hierarchical loss function to the training of prototypical networks, combined with a method to aggregate prototypes hierarchically, mirroring the structure of a predefined musical instrument hierarchy. These extensions require no changes to the network architecture and new levels can be easily added or removed. Compared to a non-hierarchical few-shot baseline, our method leads to a significant increase in classification accuracy and significant decrease mistake severity on instrument classes unseen in training. |
Databáze: |
arXiv |
Externí odkaz: |
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