ATGNN: Audio Tagging Graph Neural Network
Shubhr Singh Christian J. Steinmetz Emmanouil Benetos Huy Phan Dan Stowell
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.