Affinity Mixup for Weakly Supervised Sound Event Detection

Abstract

The weakly supervised sound event detection (WSSED) 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 (AM) 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 (AMN) improves over state-of-the-art techniques event-F1 scores by 8.2%.