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arXiv · 2007.00144

A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition

Anurag Kumar·Vamsi Krishna Ithapu
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Abstract

An important problem in machine auditory perception is to recognize and detect sound events. In this paper, we propose a sequential self-teaching approach to learning sounds. Our main proposition is that it is harder to learn sounds in adverse situations such as from weakly labeled and/or noisy labeled data, and in these situations a single stage of learning is not sufficient. Our proposal is a sequential stage-wise learning process that improves generalization capabilities of a given modeling system. We justify this method via technical results and on Audioset, the largest sound events dataset, our sequential learning approach can lead to up to 9% improvement in performance. A comprehensive evaluation also shows that the method leads to improved transferability of knowledge from previously trained models, thereby leading to improved generalization capabilities on transfer learning tasks.

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BibTeXRIS

Anurag Kumar, Vamsi Krishna Ithapu. 2020-06-30. A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition. https://arxiv.org/abs/2007.00144

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