一种提高声音事件识别泛化能力的顺序自学习方法
A Sequential Self Teaching Approach for Improving Generalization in Sound Event Recognition
摘要：
机器听觉感知中的一个重要问题是识别和检测声音事件。在本文中，我们提出了一个顺序的自我教学方法来学习声音。我们的主要主张是，在不利的情况下，例如从弱标记和/或噪声标记的数据中学习声音是很困难的，在这些情况下，单个学习阶段是不够的。我们的建议是一个连续的阶段明智的学习过程，提高了泛化能力的一个给定的建模系统。我们通过技术结果证明了这种方法的合理性，在最大的声音事件数据集Audioset上，我们的顺序学习方法可以使性能提高9%。一个全面的评估还表明，该方法导致从以前训练的模型的知识的可转移性的改善，从而导致迁移学习任务的泛化能力的改善。
英文摘要：
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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