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arXiv · 2410.10994

GraFPrint: A GNN-Based Approach for Audio Identification

Aditya Bhattacharjee·Shubhr Singh·Emmanouil Benetos
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Abstract

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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Aditya Bhattacharjee, Shubhr Singh, Emmanouil Benetos. 2024-10-14. GraFPrint: A GNN-Based Approach for Audio Identification. https://arxiv.org/abs/2410.10994

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