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arXiv · 2106.11233

Affinity Mixup for Weakly Supervised Sound Event Detection

Mohammad Rasool Izadi·Robert Stevenson·Laura N. Kloepper
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Abstract

The weakly supervised sound event detection problem is the task of predicting the presence of sound events and their corresponding starting and ending points in a weakly labeled dataset. A weak dataset associates each training sample (a short recording) to one or more present sources. Networks that solely rely on convolutional and recurrent layers cannot directly relate multiple frames in a recording. Motivated by attention and graph neural networks, we introduce the concept of an affinity mixup to incorporate time-level similarities and make a connection between frames. This regularization technique mixes up features in different layers using an adaptive affinity matrix. Our proposed affinity mixup network improves over state-of-the-art techniques event-F1 scores by $8.2\%$.

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Mohammad Rasool Izadi, Robert Stevenson, Laura N. Kloepper. 2021-06-21. Affinity Mixup for Weakly Supervised Sound Event Detection. https://arxiv.org/abs/2106.11233

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