Deep learning models such as CNNs and Transformers have achieved impressive
performance for end-to-end audio tagging. Recent works have shown that despite
stacking multiple layers, the receptive field of CNNs remains severely limited.
Transformers on the other hand are able to map global context through
self-attention, but treat the spectrogram as a sequence of patches which is not
flexible enough to capture irregular audio objects. In this work, we treat the
spectrogram in a more flexible way by considering it as graph structure and
process it with a novel graph neural architecture called ATGNN. ATGNN not only
combines the capability of CNNs with the global information sharing ability of
Graph Neural Networks, but also maps semantic relationships between learnable
class embeddings and corresponding spectrogram regions. We evaluate ATGNN on
two audio tagging tasks, where it achieves 0.585 mAP on the FSD50K dataset and
0.335 mAP on the AudioSet-balanced dataset, achieving comparable results to
Transformer based models with significantly lower number of learnable
parameters.

本文介绍了一种名为 ATGNN 的新颖图神经网络架构，通过将谱图视作图结构来处理，实现了 CNN 的能力与图神经网络的全局信息共享能力的结合，并映射了可学习的类别嵌入和相应谱图区域之间的语义关系，通过在两个音频标记任务中的表现，发现 ATGNN 在 FSD50K 数据集上达到 0.585 的 mAP，在 AudioSet-balanced 数据集上达到 0.335 的 mAP，与基于 Transformer 的模型相比具有相当的结果，但参数数量显著较少。