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

Jul 12, 2020

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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 learn sounds. Our main proposition is that it is harder to learn sounds in adverse situations such as from weakly labeled 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 via technical results. On Audioset, the largest sound events dataset, our sequential learning approach can lead to up to 9 improved transferability of knowledge from previously trained models, thereby leading to improved generalization capabilities on transfer learning tasks as well.

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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