用图神经网络生成抗干扰音频指纹，识别更准更快。
GraFPrint: A GNN-Based Approach for Audio Identification
- 构建时频图的k近邻图，用图卷积学习局部全局特征
- 自监督对比学习提升对环境噪声的鲁棒性，识别准确率高
- 轻量可扩展，适合大规模音频库实时应用
本文提出GraFPrint，一种基于图神经网络（GNN）的音频识别框架，利用GNN的结构学习能力生成鲁棒音频指纹。方法从时频表示构建k-近邻（k-NN）图，并采用最大相对图卷积编码局部与全局信息。网络通过自监督对比学习训练，优化特征表示以增强对环境畸变的鲁棒性。GraFPrint在大规模数据集上多个粒度层级均表现优异，兼具轻量与可扩展性，适用于包含海量参考数据库的实际应用场景。
原文摘要 · Abstract (English)
This paper introduces GraFPrint, an audio identification framework that leverages the structural learning capabilities of Graph Neural Networks (GNNs) to create robust audio fingerprints. Our method constructs a k-nearest neighbor (k-NN) graph from time-frequency representations and applies max-relative graph convolutions to encode local and global information. The network is trained using a self-supervised contrastive approach, which enhances resilience to ambient distortions by optimizing feature representation. GraFPrint demonstrates superior performance on large-scale datasets at various levels of granularity, proving to be both lightweight and scalable, making it suitable for real-world applications with extensive reference databases.
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