Multi-Scale Gated Attention for Weakly Labelled Sound Event Detection
Zhenwei Hou, Liping Yang · 2021
Convolutional recurrent neural network (CRNN) with attention mechanisms has achieved the state-of-the-art performance in weakly labelled sound event detection (SED). We find the state-of-the-art CRNN only use single time-frequency scale to extract features of sound events. In this paper, we propose a multi-scale gated attention (MGA) CRNN for weakly labelled SED to extract more useful representations. MGA is concatenated by multi-scale context gating (CG), "Squeeze and Excitation" (SE) block and two convolutional layers. Multi-scale CG extracts multi-scale information of sound events and calculates attention weights at multiple resolutions for each feature map. The subsequent SE block learns the inter-dependencies between the channels of feature maps. MGA extract discriminative representations by selecting significant spatial feature and channel feature. Our method has achieved state-of-the-art results with 61.1% audio tagging F1 score on DCASE2017 Task 4 evaluation set and 32.16% event-based F1 score on DCASE2018 Task 4 evaluation set.