LHGNN: Local-Higher Order Graph Neural Networks For Audio Classification and Tagging

• URL: http://arxiv.org/abs/2501.03464v2
• Date: Wed, 29 Jan 2025 12:22:49 GMT
• Title: LHGNN: Local-Higher Order Graph Neural Networks For Audio Classification and Tagging
• Authors: Shubhr Singh, Emmanouil Benetos, Huy Phan, Dan Stowell,
• Abstract summary: This work introduces the Local- Higher Order Graph Neural Network (LHGNN), a graph based model that enhances feature understanding. Evaluation of the model on three publicly available audio datasets shows that it outperforms Transformer-based models across all benchmarks.
• Score: 23.464493621300242
• License:
• Abstract: Transformers have set new benchmarks in audio processing tasks, leveraging self-attention mechanisms to capture complex patterns and dependencies within audio data. However, their focus on pairwise interactions limits their ability to process the higher-order relations essential for identifying distinct audio objects. To address this limitation, this work introduces the Local- Higher Order Graph Neural Network (LHGNN), a graph based model that enhances feature understanding by integrating local neighbourhood information with higher-order data from Fuzzy C-Means clusters, thereby capturing a broader spectrum of audio relationships. Evaluation of the model on three publicly available audio datasets shows that it outperforms Transformer-based models across all benchmarks while operating with substantially fewer parameters. Moreover, LHGNN demonstrates a distinct advantage in scenarios lacking ImageNet pretraining, establishing its effectiveness and efficiency in environments where extensive pretraining data is unavailable.

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