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- W3198882010 abstract "Transfer learning is critical for efficient information transfer across multiple related learning problems. A simple, yet effective transfer learning approach utilizes deep neural networks trained on a large-scale task for feature extraction. Such representations are then used to learn related downstream tasks. In this paper, we investigate transfer learning capacity of audio representations obtained from neural networks trained on a large-scale sound event detection dataset. We build and evaluate these representations across a wide range of other audio tasks, via a simple linear classifier transfer mechanism. We show that such simple linear transfer is already powerful enough to achieve high performance on the downstream tasks. We also provide insights into the attributes of sound event representations that enable such efficient information transfer." @default.
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- W3198882010 date "2021-08-30" @default.
- W3198882010 modified "2023-10-01" @default.
- W3198882010 title "Do Sound Event Representations Generalize to Other Audio Tasks? A Case Study in Audio Transfer Learning" @default.
- W3198882010 doi "https://doi.org/10.21437/interspeech.2021-347" @default.
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