Title: GRAFTa: A Graph Attentional Few-Shot Audio Tagging Network
Primary area: transfer learning, meta learning, and lifelong learning
Keywords: few-shot learning, multi-label classification, audio tagging, meta learning, episodic training, prototype learning, graph neural networks, graph attention networks, compositional representations, benchmark datasets, subset prototypes

Abstract: Audio tagging models based on CNNs and Transformers achieve strong results, but they rely on large labeled datasets and heavy computation, and struggle with the irregular structure of spectrograms. We propose GRAFTa, a Graph Attentional Few-shot Audio Tagging framework that represents spectrogram regions as nodes in a dynamically constructed graph, enabling attention to capture both local and long-range acoustic dependencies. To support multi-label generalization from few examples, GRAFTa constructs compositional subset prototypes: embeddings for both individual labels and frequently co-occurring label subsets mined from the support set. We evaluate GRAFTa in episodic few-shot settings on FSD50K and AudioSet-Balanced, achieving 0.2296 and 0.2221 mean average precision (mAP), respectively. To our knowledge, this is the first work to establish benchmarks for few-shot multi-label audio tagging on these datasets, highlighting both the promise of graph-based compositional prototypes and the unique challenges of this setting.

TL;DR: We introduce GRAFTa, a graph-based few-shot model for multi-label audio tagging, benchmark it on FSD50K and AudioSet, and show insights into label co-occurrence and shot scaling.