Do Sound Event Representations Generalize To Other Audio Tasks? A Case Study In Audio Transfer Learning

Interspeech

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; the extracted representations are then used to learn related downstream tasks. In this paper, we investigate the transfer learning capacity of audio representations obtained from neural networks trained on a large-scale sound event detection dataset. We evaluate these representations across a wide range of other audio tasks by training simple linear classifiers for them, and show that such a simple mechanism of transfer learning is already powerful enough to achieve high performance on the downstream tasks. We also provide insights into the properties of the learned sound event representations that enable such efficient information transfer.

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