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- W4310609304 abstract "With the construction of smart cities, the research on Environmental Sound Classification (ESC) has been further developed, and good results have been achieved in the existing large network models, but due to its large model, it is not conducive to deployment on small and embedded devices. To this end, we apply knowledge distillation to the Environmental Sound Classification (ESC) task, transferring the knowledge learned from audio data through a large network model into a lightweight network model to improve lightweight network training. On this basis, we improved the knowledge distillation method, and the lightweight network model can obtain more information from different layers of the large network model. We found that our model outperformed existing models, achieving 87% accuracy on ESC-50." @default.
- W4310609304 created "2022-12-13" @default.
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- W4310609304 date "2022-10-21" @default.
- W4310609304 modified "2023-09-28" @default.
- W4310609304 title "Environmental Sound Classification Based on Knowledge Distillation" @default.
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- W4310609304 doi "https://doi.org/10.1109/icsp56322.2022.9965274" @default.
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